{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Languages of Journals using OJS " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This link navigates to a Google doc with examples of online journals using OJS to publish open access articles in **60 different languages**.
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Notebook objectives:\n", "1. [Obtain gcld3 language predictions for the abstracts of the most recent 100 articles published in a sample of 22,561 ISSN-verified journals actively using OJS.](#gcld3)\n", " * Google's Compact Language Detector v3 (https://github.com/google/cld3), a pretrained model for language classification\n", "

\n", "2. [Verify gcld3 language predictions for each journal using a variety of heuristics, including:](#verify)\n", " * Checking language predictions against journal top-level domains (e.g., \"es\" for Spanish and \".es\" for Spain)\n", " * Cross-checking language predictions for article abstracts, article titles, and journal titles\n", " * Manually checking frequent misclassifications, such as Japanese, Scottish Gaelic, and Afrikaans\n", "

\n", "3. [Visualize journals by their primary language of publishing:](#primary)\n", " * [Top 10 languages](#top10)\n", " * [All languages in the dataset](#all)\n", "

\n", "4. [Classify journals based on whether they publish in multiple languages.](#multi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import packages:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "from collections import Counter\n", "from lxml import html\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "import json\n", "import time\n", "import re\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Google's Compact Language Detector v3 (gcld3) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize gcld3:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gcld3\n", "classifier = gcld3.NNetLanguageIdentifier(min_num_bytes=0, max_num_bytes=10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store a list of gcld3 language codes corresponding to the 60 different publishing languages in OJS use (except Faroese and Balochi, which are unsupported by gcld3):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "known_langs = ['af', 'al', 'ar', 'bg', 'bg-Latn', 'bs', 'ca', 'cs', 'da', 'de', 'el', 'el-Latn', 'en', 'es', 'et', \n", " 'eu','fa', 'fi', 'fil', 'fr', 'gd', 'gl', 'hi', 'hi-Latn', 'hr', 'hu', 'hy', 'id', 'ig', 'is', 'it', \n", " 'ja','ja-Latn', 'ka', 'kk', 'ko', 'ku', 'lt', 'mk', 'ms', 'ne', 'nl', 'no', 'pl', 'pt', 'ro', 'ru', \n", " 'ru-Latn','si','sk', 'sl', 'sr', 'sv', 'sw', 'ta', 'th', 'tr', 'uk', 'ur', 'uz', 'vi', 'zh', 'zh-Latn']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a function that:\n", "
\n", "1. Opens and streams a messy 28.5 GB file of scraped and printed OAI-PMH article metadata [title, description, subject, language, source...] for all articles published in **22,561 unique OJS contexts, or journals**;\n", "

\n", "2. Filters journals by ISSN and filters language predictions by inclusion in the list of known OJS languages (to avoid misclassification);\n", "

\n", "3. Passes the 'description' values (article abstracts) for the **most recent 100 articles published in each journal** to gcld3 to generate lists of predicted languages for each journal;\n", "

\n", "4. Returns a dict mapping journal issn to a list of gcld3-predicted language codes for article abstracts:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def classify_abstracts(path_to_dump, classifier, issn_filter, lang_filter):\n", " \n", " metadata_pattern = '.+'\n", " issn2langs = defaultdict(list) #defaultdict of lists\n", " dcount = defaultdict(int)\n", " #Full processing\n", " \n", " with open(path_to_dump, 'r') as f:\n", " article_count = 0\n", " \n", " for line in f:\n", " content = re.search(metadata_pattern, line, re.MULTILINE | re.DOTALL)\n", " if content:\n", " tree = html.fromstring(content.group())\n", "\n", " for article in tree.xpath('//metadata'):\n", " article_count += 1\n", " \n", " for source in article.xpath('.//source'):\n", " source_copy = str(source.text)\n", " source_copy = re.sub('\\s', '', source_copy)\n", " if source_copy in issn_filter:\n", " issn = source_copy\n", " for description in article.xpath('.//description'):\n", " if dcount[issn] < 100 and description is not None: #if <100 abstracts have been classified\n", " pred_ = classifier.FindLanguage(text=description.text) #run gcld3\n", " if pred_.is_reliable and pred_.language in lang_filter: \n", " #if the language prediction is reliable and in a known OJS language\n", " issn2langs[issn].append(pred_.language)\n", " #append to a list of language predictions for the journal\n", " dcount[issn] += 1\n", " del pred_\n", " \n", " while tree.getprevious() is not None:\n", " del tree.getparent()[0]\n", " del content\n", " \n", " print(f\"Articles scanned: {article_count}\")\n", " print(f\"Journals classified: {len(issn2langs)}\")\n", " print(f\"Missing issns: {set(issn_filter) - set(list(issn2langs.keys()))}\")\n", " return issn2langs" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "path_to_dump = os.path.join('data', 'datadump.txt')\n", "path_to_beacon = os.path.join('data', 'beacon_active.csv')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22809\n" ] } ], "source": [ "with open(path_to_beacon, 'r') as f:\n", " df = pd.read_csv(f)\n", "df = df[~df['issn_1'].duplicated()]\n", "issn_filter = [i for i in df['issn_1'].tolist() if isinstance(i, str)]\n", "print(len(issn_filter))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Articles scanned: 7960979\n", "Journals classified: 22559\n", "Missing issns: {'2501-9430', '1047-6857', '2364-3714', '2525-8281', '1856-6073', '1678-9059', '2451-3962', '2358-1069', '2540-8445', '2721-5148', '2798-6241', '2655-2469', '2721-6020', '2734-9314', '2528-4967', '2734-9330', '1411-6340', '2775-5592', '2302-8432', '2355-1720', '2477-5029', '2503-3417', '1668-8708', '2615-6911', '2721-9976', '2598-0637', '1096-746X', '2451-1862', '2222-6737', '2775-1937', '2153-4012', '2600-5689', '1411-2280', '2722-3736', '2086-7840', '2183-0134', '1747-7387', '1693-4458', '2538-399X', '2501-2428', '0436-0265', '2716-3679', '2316-5324', '2655-948X', '2597-8985', '2621-783X', '2722-7960', '2620-5068', '1549-4497', '2598-9626', '2722-7111', '2723-1186', '2580-3123', '2460-352X', '2686-4908', '2622-5867', '1693-461X', '1754-4270', '2685-2799', '2614-4042', '2085-8744', '0124-1192', '2359-1382', '2622-6138', '2602-0254', '2668-9928', '1216-6804', '0103-4979', '0797-8952', '2215-9827', '2723-5319', '2339-1499', '1981-6979', '2656-1565', '1705-9100', '2337-5973', '2549-4317', '2291-8639', '2027-7636', '1987-037X', '1314-586X', '2745-6889', '2723-4088', '2442-9910', '2541-7207', '2501-5915', '2655-6065', '2549-2454', '2716-5043', '2338-0683', '2490-1199', '2715-2138', '2715-5889', '1997-3837', '0258-2724', '2447-7028', '2085-8205', '2238-8494', '2501-7136', '2334-1645', '2526-7744', '2722-0516', '2668-1056', '2375-7817', '2331-6950', '2722-2012', '0315-3681', '0216-7395', '2089-8118', '2734-5475', '2714-6278', '2734-9349', '2621-0622', '0798-0329', '2599-0543', '1978-0125', '2501-1111', '2708-7530', '1693-7619', '2654-5667', '2229-5674', '2735-9417', '2238-944X', '2525-2003', '1806-4280', '2675-4142', '2721-5016', '2502-471X', '2655-6324', '2732-3587', '1978-2403', '2723-7443', '2477-5258', '2620-5726', '2710-0898', '0216-7298', '2489-5512', '2501-9988', '2252-4797', '2350-0123', '2715-4971', '2579-9193', '2252-5262', '2715-1018', '2338-3720', '1081-1451', '1679-4605', '2094-1277', '0940-7855', '1982-6109', '2184-7193', '2615-2037', '2746-6434', '1412-0712', '2537-1754', '2294-9844', '2596-1837', '2668-9758', '2716-408X', '2722-5089', '1979-052X', '2501-1235', '2720-9903', '2406-8802', '2501-8590', '2460-8076', '2366-9217', '2461-0623', '2559-7914', '2698-5446', '1531-0167', '2580-6912', '2359-5965', '2764-1066', '2721-8511', '2685-6123', '2088-4605', '2548-7523', '2312-2528', '2734-9306', '2447-0899', '1980-5772', '2337-568X', '2302-934X', '2763-8669', '2595-9026', '2447-3472', '0126-074X', '2620-5505', '2711-4716', '2734-9357', '2460-3236', '2549-6778', '1412-226X', '2356-5225', '2684-9062', '2303-1409', '2614-719X', '2721-3315', '2622-9765', '2541-6030', '0125-9326', '2527-5445', '2540-9417', '2007-3380', '2699-5433', '2594-813X', '2179-8168', '1678-4944', '1411-2973', '2623-162X', '2174-7210', '2561-7141', '2597-5277', '2599-0551', '2716-0394', '2549-3485', '2615-8396', '2549-3361', '2528-1569', '2076-6327', '0208-5712', '2501-9120', '2686-2565', '1693-024X', '2797-5967', '2198-9397', '2089-6980', '2710-091X', '2406-8616', '2685-712x', '2601-971X', '2616-2504', '1669-726X', '2540-9808', '1412-4246', '2807-9256', '2408-350X', '2685-6425', '2616-6291', '2601-1972', '2614-5944', '2628-7129', '2685-161X', '1806-1230', '2807-887X', '2303-002X', '2685-5070', '0124-0625', '2252-7141'}\n", "CPU times: user 1h 22min 58s, sys: 55 s, total: 1h 23min 53s\n", "Wall time: 1h 23min 54s\n" ] } ], "source": [ "%time issn2langs = classify_abstracts(path_to_dump, classifier, issn_filter, lang_filter=known_langs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sanity check:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2715-2502\n", "['id', 'en', 'id', 'id', 'id', 'id', 'id', 'en', 'id', 'en', 'en', 'id', 'id', 'ms', 'id', 'en', 'id', 'en', 'id', 'id', 'en']\n" ] } ], "source": [ "with open(os.path.join('data', 'issn2langs.json'), 'w') as outfile:\n", " json.dump(issn2langs, outfile)\n", "print(type(issn2langs))\n", "for k, v in issn2langs.items():\n", " print(k) #issn for one journal\n", " print(v) #list of gcld3 language classifications for most recent 100 or fewer articles published in journal\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Verifying gcld3-predicted language codes for each journal " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data\n", "Create a DataFrame, `AA`, that joins gcld3-predicted language codes with data from the beacon. This DataFrame will be used to verify gcld3 language predictions for each journal." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#reload dict {issn: list of gcld3-predicted language codes}\n", "with open(os.path.join('data','issn2langs.json'), 'r') as infile:\n", " issn2langs = json.load(infile)\n", "#load dict {issn: text sample of concatenated titles and abstracts}\n", "with open(os.path.join('data','issn2payload.json'), 'r') as infile:\n", " issn2payload = json.load(infile)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#{issn: primary language}\n", "l1 = {}\n", "for k, v in issn2langs.items():\n", " l1[k] = Counter(v).most_common(1)[0][0]\n", "#{issn: secondary language}\n", "l2 = {}\n", "for k, v in issn2langs.items():\n", " try:\n", " l2[k] = Counter(v).most_common(2)[1][0]\n", " except IndexError:\n", " l2[k] = None\n", " continue" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# issn | primary language predicted by gcld3 | secondary language predicted by gcld3\n", "dfL = pd.DataFrame({'issn': l1.keys(),\n", " 'pred_1': l1.values(),\n", " 'pred_2': l2.values(),\n", " })\n", "# issn | text sample of concatenated titles and abstracts\n", "dfP = pd.DataFrame({'issn': issn2payload.keys(),\n", " 'text':issn2payload.values()})\n", "# issn | primary language | secondary language | text sample\n", "dfA = pd.merge(dfL, dfP, how='outer')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#load beacon data\n", "with open('data/beacon_active.csv', 'r') as infile:\n", " bA = pd.read_csv(infile)\n", "#select beacon columns useful for language verification \n", "bA = bA[['context_name', 'issn_1', 'issn_2', 'country_consolidated', 'journal_url']].copy()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#rename then merge into AA\n", "bA.rename(columns = {'issn_1':'issn',\n", " 'issn_2':'issn_alt',\n", " 'country_consolidated':'tld'}, inplace = True)\n", "AA = pd.merge(dfA, bA, on='issn')\n", "#AA.to_csv('data/AA.csv')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['id', 'af', 'en', 'ms', 'es', 'ar', 'pt', 'th', 'ca', 'el', 'uk',\n", " 'it', 'fr', 'is', 'de', 'no', 'ja', 'ru', 'tr', 'sv', 'hi', 'pl',\n", " 'sr', 'sl', 'cs', 'da', 'vi', 'lt', 'hu', 'hr', 'mk', 'zh', 'ta',\n", " 'kk', 'sw', 'gd', 'sk', 'et', 'fa', 'bs', 'eu', 'ro', 'bg', 'fil',\n", " 'ka', 'hy', 'uz', 'nl', 'fi', 'ne', 'ig', nan], dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#deduplicate issns\n", "AA = AA[~AA['issn'].duplicated()]\n", "#lowercase top-level domains\n", "AA['tld'] = AA['tld'].str.lower()\n", "AA['pred_1'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Helper functions:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for applying gcld3 to journal titles and article-level text samples:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def tag_language(s):\n", " l = classifier.FindLanguage(text=s)\n", " if l.is_reliable:\n", " return l.language" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "AA['title_language'] = pd.Series([tag_language(s) if isinstance(s, str) else None for s in AA['context_name'].tolist()])\n", "AA['text_language'] = pd.Series([tag_language(s) if isinstance(s, str) else None for s in AA['text'].tolist()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for adding journal issns and double-checked language predictions to a cleaned dict `clean_d`:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def add2dict(d, issns, langs, corrections=False):\n", " count = 0\n", " corrs = 0\n", " for issn, l2 in zip(issns, langs):\n", " if corrections:\n", " d[issn] = l2\n", " corrs += 1\n", " continue\n", " else:\n", " if issn in d:\n", " continue\n", " else:\n", " d[issn] = l2\n", " count += 1\n", " print(f\"{count} journal(s) added;\\n{corrs} journals corrected;\\n{len(d)} journals cleaned and stored in total\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "clean_d = {}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Heuristic approach to language verification:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Add a Faroese journal and a Balochi journal, both of which are misclassified in the data set:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 journal(s) added;\n", "0 journals corrected;\n", "2 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=['2445-6144', '2710-4850'],\n", " langs=['Faroese', 'Balochi'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. If a journal has a nonnull `pred_1` value but a null `pred_2` value, it is probably publishing in language `pred_1` (because the most recent 100 article abstracts were all tagged with a single language code, `pred_1`):" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5791 journal(s) added;\n", "0 journals corrected;\n", "5793 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'].notnull()) & (AA['pred_2'].isna())].issn.tolist(),\n", " langs=AA[(AA['pred_1'].notnull()) & (AA['pred_2'].isna())].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. If gcld3 predicts the same primary language for a journal's title metadata `title_language`, article metadata `text_language`, and abstracts from the most recent 100 articles published `pred_1`, add the journal:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1172 journal(s) added;\n", "0 journals corrected;\n", "6965 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == AA['text_language']) & (AA['pred_1'] == AA['title_language'])].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == AA['text_language']) & (AA['pred_1'] == AA['title_language'])].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4. If the journal's secondary language `pred_2` matches its top level domain `tld` and the primary language predicted by gcld3 `pred_1` is not English, `pred_1` is probably an error, so add `pred_2`:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "116 journal(s) added;\n", "0 journals corrected;\n", "7081 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_2'] == AA['tld']) & (AA['pred_1'] != 'en')].issn.tolist(),\n", " langs=AA[(AA['pred_2'] == AA['tld']) & (AA['pred_1'] != 'en')].pred_2.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 5. Check if journals with an `af` Afrikaans primary language classification actually have an African top-level domain `tld`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "#store list of African country top-level domains\n", "af_tlds = ['AO','BI','BJ','BF','BW','CF','CI','CM','CD','CG','KM','CV','ER','ET','GA','GH',\n", " 'GN','GM','GW','GQ','KE','LR','LS','MG','ML','MZ','MR','MU','MW','NA','NE','NG',\n", " 'RW','SD','SN','SL','SO','SS','ST','SZ','SC','TD','TG','TZ','UG','ZA','ZM','ZW']\n", "AA['is_af'] = AA['tld'].apply(lambda x: any([i.lower() for i in af_tlds if i.lower() == x]))\n", "#AA[((AA['pred_1'] == 'af') | (AA['pred_2'] == 'af')) & (AA['is_af'] == True)]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 journal(s) added;\n", "0 journals corrected;\n", "7086 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=['1013-1116', '0041-4751', '0041-476X', '0254-3486', '2006-1390'],\n", " langs=['af', 'af', 'af', 'af', 'en'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6. Assuming that all remaining `pred_1` == `af` are misclassifications, add `pred_2` instead:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70 journal(s) added;\n", "0 journals corrected;\n", "7156 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'af')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'af')].pred_2.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 7. If journals with an `es` Spanish primary language classification also have a Latin American top-level domain `tld`, they are probably publishing in Spanish:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 20123\n", "True 2468\n", "Name: is_latam, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "latam_tlds = ['AW','AR','AG','BS','BZ','BO',#'BR', not brazil! bc portuguese\n", " 'BB','CL','CO','CR','CU','CW','KY','DM','DO','EC','GD','GT','GY','HN','HT','JM','KN','LC',\n", " 'GP','MX','NI','PA','PE','PR','PY','SV','SR','AN','SX','TC','TT','UY','VC','VE','VG','VI']\n", "AA['is_latam'] = AA['tld'].apply(lambda x: any([i.lower() for i in latam_tlds if i.lower() == x]))\n", "AA['is_latam'].value_counts()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1681 journal(s) added;\n", "0 journals corrected;\n", "8837 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'es') & (AA['is_latam'] == True)].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'es') & (AA['is_latam'] == True)].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 8. Japanese journals, jointly identified by language prediction `ja` and top-level domain `jp`:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 journal(s) added;\n", "0 journals corrected;\n", "8839 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'ja') & (AA['tld'] == 'jp')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'ja') & (AA['tld'] == 'jp')].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 9. Japanese again, with the assumption that all remaining `pred_1` == `ja` are misclassifications, so add `pred_2` instead:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63 journal(s) added;\n", "0 journals corrected;\n", "8902 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'ja')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'ja')].pred_2.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 10. Manually enter correct language codes for misclassified French examples:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#pd.set_option(\"display.max_rows\", None)\n", "#AA[(AA['pred_1'] == 'fr') & (AA['pred_2'].notnull())]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13 journal(s) added;\n", "0 journals corrected;\n", "8915 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=['0008-4123', '2341-0868', '2595-6752', '1026-2881', '2368-8076', '2665-7716', '0702-7818',\n", " '1496-7308', '0002-4805', '1544-4953', '1499-6677', '0705-3657', '2605-0285'],\n", " langs=['en', 'es', 'pt', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 11. Malay journals, jointly identified by primary language prediction `ms` and top-level domain `my`:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36 journal(s) added;\n", "0 journals corrected;\n", "8951 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'ms') & (AA['tld'] == 'my')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'ms') & (AA['tld'] == 'my')].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12. Brazilian journals, jointly identified by primary language prediction `pt` and top-level domain `br`:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1762 journal(s) added;\n", "0 journals corrected;\n", "10713 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'pt') & (AA['tld'] == 'br')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'pt') & (AA['tld'] == 'br')].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 13. German journals, jointly identified by primary language prediction `de` and top-level domain `de`:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "69 journal(s) added;\n", "0 journals corrected;\n", "10782 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'de') & (AA['tld'] == 'de')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'de') & (AA['tld'] == 'de')].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 14. Hindi journals, jointly identified by primary language prediction `hi` | `hi-Latn` and top-level domain `in`:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 journal(s) added;\n", "0 journals corrected;\n", "10784 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_1'] == 'hi') | (AA['pred_1'] == 'hi-Latn') & (AA['tld'] == 'in')].issn.tolist(),\n", " langs=AA[(AA['pred_1'] == 'hi') | (AA['pred_1'] == 'hi-Latn') & (AA['tld'] == 'in')].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 14. Manually check OJS contexts with a Philippines top-level domain (.ph):" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "#AA[(AA['tld'] == 'ph') & (AA['pred_2'].notnull())]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "2 journals corrected;\n", "10784 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['0012-2858', '2244-6001'],\n", " langs=['en', 'fil'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 15. Manually check OJS contexts with a Danish top-level domain (.dk):" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#AA[(AA['tld'] == 'dk') & (AA['pred_2'].notnull())]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9 journal(s) added;\n", "0 journals corrected;\n", "10793 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=['2596-6200', '0909-0533', '2446-0591', '2446-3981', '2597-0704', \n", " '1603-8509', '2246-2589', '2244-9140', '1904-5565', '0029-1528'],\n", " langs=['da', 'da', 'da', 'en', 'da', 'da', 'da', 'da', 'da', 'da'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 16. Manually check musicological journals, becuase musical notation makes problems for gcld3:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "#AA[AA['context_name'].str.contains('musi[ck]', regex=True, case=False)]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 journal(s) added;\n", "0 journals corrected;\n", "10795 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=['0354-818X', '2312-2528'],\n", " langs=['sr', 'en'])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "1 journals corrected;\n", "10795 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['0011-3735'], #current musicology, which gcld3 classified as Japanese and Gaelic\n", " langs=['en'], #https://currentmusicology.columbia.edu/\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 17. Manually check all Scottish Gaelic `gd` contexts:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "#AA[(AA['pred_1'] == 'gd') | (AA['pred_2'] == 'gd')]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 journal(s) added;\n", "0 journals corrected;\n", "10799 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['1754-4270', #perhaps the only true Scottish Gaelic journal\n", " '0035-6867', '2675-1127', '0957-5286'],\n", " langs=['gd', 'it', 'pt', 'en'])" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "3 journals corrected;\n", "10799 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['1805-9511', '0252-9076', '2563-562X'],\n", " langs=['cs', 'es', 'en'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 18. Manually check contexts with a Czech top-level domain `cz` :" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "#AA[AA['tld'] == 'cz']" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "9 journals corrected;\n", "10804 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['0009-2770', '2336-2766', '2336-3630', '1805-9511', '2336-4378',\n", " '1802-3983', '1804-5383', '1804-6665', '2694-9288'],\n", " langs=['cs', 'cs', 'uk', 'cs', 'cs', 'cs', 'cs', 'cs', 'cs'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 19. Manually check contexts with an Albanian top-level domain `al` :" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "#pd.set_option(\"display.max_rows\", None)\n", "#AA[AA['tld']=='al']" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "1 journals corrected;\n", "10804 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['2523-6636'],\n", " langs=['sq'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 20. Manually check contexts with a Pakistani top-level domain `pk` :" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "#AA[AA['tld']=='pk']" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "44 journals corrected;\n", "10815 journals cleaned and stored in total\n" ] } ], "source": [ "#The output below is an error -- 11 journals added, 33 journals corrected\n", "add2dict(clean_d,\n", " issns=['2663-6255', '0430-4055', '1813-775X', '2707-1200', '2411-6211', '1995-7904', '2520-5021', \n", " '1998-4472', '2708-8235', '2073-5146', '2707-6903', '2664-4959', '2708-8847', '1816-5389', \n", " '2709-8885', '2664-1461', '2305-1345', '2708-6577', '2618-1355', '1818-9296', '2664-0023', \n", " '2709-6076', '2709-7641', '2709-4022', '2709-4162', '2710-0227', '2710-0812', '2410-8065', \n", " '2521-408X', '2707-6288', '2617-9075', '2305-154X', '2710-2475', '2710-5180', '2707-7225', \n", " '2523-0093', '2521-8948', '2788-4627', '2709-7617', '2073-3674', '2413-7480', '2415-5500', \n", " '2519-6618', '2518-5330'],\n", " langs=['ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', \n", " 'ur', 'Balochi', 'ur', 'ur', 'ur', 'ur', 'ar', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', \n", " 'ur', 'ur', 'ur', 'ur', 'ar', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ur', 'ar', 'ur', 'ur'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 21. Manually check journals that were predicted as Arabic `ar` :" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "#AA[(AA['pred_1'] == 'ar') | (AA['pred_2'] == 'ar')]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "25 journals corrected;\n", "10835 journals cleaned and stored in total\n" ] } ], "source": [ "#The output below is an error -- 20 journals added, 5 journals corrected\n", "add2dict(clean_d,\n", " issns=['1693-3257', '2700-8355', '2600-7398', '2410-1036', '2600-7398', '2223-859X', '1996-9546',\n", " '2071-9728', '1026-3748', '1026-3721', '1995-8005', '2460-5360', '2716-5515', '0552-265X',\n", " '1994-473X', '2663-7405', '2437-0789', '2522-3259', '2522-6460', '2421-9843', '2520-7431',\n", " '2664-4673', '2706-9524', '1658-7030', '1658-3116'],\n", " langs=['ar', 'ar', 'ar', 'ku', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'id', 'ar', 'ar', 'ar',\n", " 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar', 'ar'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 22. Manually check contexts with a Belarusian top-level domain `by` :" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "#AA[AA['tld']=='by']" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 journal(s) added;\n", "3 journals corrected;\n", "10837 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(clean_d,\n", " issns=['2222-8853', '2789-195X', '2415-7198'],\n", " langs=['by', 'en', 'uk'],\n", " corrections=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 23. If a journal's primary language prediction `pred_1` and top-level domain `tld` match, add the journal:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5298 journal(s) added;\n", "0 journals corrected;\n", "16135 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[AA['pred_1'] == AA['tld']].issn.tolist(),\n", " langs=AA[AA['pred_1'] == AA['tld']].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 24. If a journal has an Eastern European top-level domain `tld` and its secondary language prediction `pred_2` matches its `title_language`, it probably publishes in its secondary language (e.g., Ukrainian) but translates article metadata into English. So, add the secondary language on the assumption that `text_language` == `en` is an error due to translated article titles and abstracts:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "ee_tlds = ['AL','AD','AM','AT','BG','BA','BY','CY','CZ','EE','GE','GR','GL','HR',\n", " 'HU','LT','LV','MD','MK','ME','PL','RO','RU','RS','SK','SI','UA']\n", "AA['is_ee'] = AA['tld'].apply(lambda x: any([i.lower() for i in ee_tlds if i.lower() == x]))\n", "#AA[(AA['pred_2'] == AA['title_language']) & (AA['pred_2'].notnull()) & (AA['is_ee'] == True)]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "127 journal(s) added;\n", "0 journals corrected;\n", "16262 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[(AA['pred_2'] == AA['title_language']) & (AA['pred_2'].notnull()) & \\\n", " (AA['is_ee'] == True)].issn.tolist(),\n", " langs=AA[(AA['pred_2'] == AA['title_language']) & (AA['pred_2'].notnull()) & \\\n", " (AA['is_ee'] == True)].pred_2.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 25. Finally, add each remaining journal's primary language prediction `pred_1` to complete `clean_d`:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6299 journal(s) added;\n", "0 journals corrected;\n", "22561 journals cleaned and stored in total\n" ] } ], "source": [ "add2dict(d=clean_d,\n", " issns=AA[AA['pred_1'].notnull()].issn.tolist(),\n", " langs=AA[AA['pred_1'].notnull()].pred_1.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the primary languages in which OJS users publish worldwide: " ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "codes = {'af':'Afrikaans', 'ar':'Arabic', 'bg':'Bulgarian', 'bg-Latn':'Bulgarian', 'bs':'Bosnian', 'by':'Belarusian',\n", " 'ca':'Catalan', 'cs':'Czech', 'da':'Danish', 'de':'German', 'el':'Greek', 'el-Latn':'Greek', 'en':'English', \n", " 'es':'Spanish', 'et':'Estonian', 'eu':'Basque', 'fa':'Persian', 'fi':'Finnish', 'fil':'Filipino',\n", " 'fr':'French', 'ga':'Irish', 'gd':'Scottish Gaelic', 'gl':'Galician', 'hi':'Hindi','hi-Latn':'Hindi',\n", " 'hr':'Croatian', 'hu':'Hungarian', 'hy':'Armenian', 'id':'Indonesian', 'ig':'Igbo', 'is':'Icelandic',\n", " 'it':'Italian', 'iw':'Hebrew', 'ja':'Japanese', 'ja-Latn':'Japanese', 'ka':'Georgian', 'kk':'Kazakh',\n", " 'ko':'Korean', 'ku':'Kurdish', 'la':'Latin', 'lt':'Lithuanian', 'lv':'Latvian', 'mk':'Macedonian',\n", " 'ms':'Malay', 'my':'Burmese', 'ne':'Nepali', 'nl':'Dutch', 'no':'Norwegian', 'pl':'Polish',\n", " 'pt':'Portuguese', 'ro':'Romanian', 'ru':'Russian', 'ru-Latn':'Russian', 'sd':'Sindhi', 'si':'Sinhala',\n", " 'sk':'Slovak', 'sl':'Slovenian', 'sq':'Albanian', 'sr':'Serbian', 'sv':'Swedish', 'sw':'Swahili', \n", " 'ta':'Tamil', 'tg':'Tajik', 'th':'Thai', 'tr':'Turkish', 'uk':'Ukrainian', 'ur':'Urdu', 'uz':'Uzbek',\n", " 'vi':'Vietnamese', 'zh':'Chinese', 'zh-Latn':'Chinese'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get primary language counts for the entire sample of journals, to be visualized using matplotlib and seaborn:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total: 22561 journals\n" ] } ], "source": [ "issn2primary = pd.DataFrame({'issn': list(clean_d.keys()),\n", " 'gcld3_code': [c if len(c) == 2 else None for c in clean_d.values()],\n", " 'language': [codes[l] if codes.get(l) else l for l in list(clean_d.values())]})\n", "issn2primary = issn2primary[issn2primary['language'].notnull()]\n", "#Get a series of value counts for the language codes\n", "ls = issn2primary['language'].value_counts(sort=True, ascending=False)\n", "print(f\"Total: {ls.sum()} journals\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(22561, 6)\n" ] }, { "data": { "text/html": [ "
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issnissn_altcontext_namejournal_urlgcld3_codelanguage
170641018-28882709-7951Diagnósticohttp://142.44.242.51/index.php/diagnosticoesSpanish
166722236-3785NaNRevista Ciências em Saúdehttp://186.225.220.186:7474/ojs/index.php/rcsf...ptPortuguese
94662077-13712077-1460Biosferahttp://21bs.ru/index.php/bioenEnglish
55602089-46862548-59702-TRIK: TUNAS-TUNAS RISET KESEHATANhttp://2trik.jurnalelektronik.com/index.php/2trikidIndonesian
98542705-0513NaN工程技术研究http://2winpub.usp-pl.com/index.php/ETRzhChinese
\n", "
" ], "text/plain": [ " issn issn_alt context_name \\\n", "17064 1018-2888 2709-7951 Diagnóstico \n", "16672 2236-3785 NaN Revista Ciências em Saúde \n", "9466 2077-1371 2077-1460 Biosfera \n", "5560 2089-4686 2548-5970 2-TRIK: TUNAS-TUNAS RISET KESEHATAN \n", "9854 2705-0513 NaN 工程技术研究 \n", "\n", " journal_url gcld3_code \\\n", "17064 http://142.44.242.51/index.php/diagnostico es \n", "16672 http://186.225.220.186:7474/ojs/index.php/rcsf... pt \n", "9466 http://21bs.ru/index.php/bio en \n", "5560 http://2trik.jurnalelektronik.com/index.php/2trik id \n", "9854 http://2winpub.usp-pl.com/index.php/ETR zh \n", "\n", " language \n", "17064 Spanish \n", "16672 Portuguese \n", "9466 English \n", "5560 Indonesian \n", "9854 Chinese " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ojsLangs = AA[['issn', 'issn_alt', 'context_name', 'journal_url']].merge(issn2primary, how='left', on='issn')\n", "ojsLangs = ojsLangs[ojsLangs['language'].notnull()].sort_values(by=['journal_url'])\n", "print(ojsLangs.shape)\n", "ojsLangs.head()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "ojsLangs.to_csv(os.path.join('data', 'OJS_languages_v3.csv'), index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar plot of the 10 primary languages in which OJS users publish their articles (*n*=21,874)
\n", "Each bar represents the proportion of journals for which the specified language is their primary publishing language." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "sns.set(font_scale=1.25, style='whitegrid')\n", "fig, ax = matplotlib.pyplot.subplots()\n", "\n", "ld = sns.barplot(x=ls.values[:10],\n", " y=ls.index[:10],\n", " orient='h',\n", " color='grey')\n", "\n", "ax.set(xlim=(0, 12000),\n", " xlabel='Active journals using OJS',\n", " ylabel='Language')#,\n", " #title='Primary language employed by journals using OJS ($\\it{n}$ = 21,874)')\n", "\n", "sns.despine(bottom=True)\n", "\n", "\n", "matplotlib.pyplot.xticks([2000, 4000, 6000, 8000, 10000],\n", " ['2,000', '4,000', '6,000', '8,000', '10,000'])\n", "\n", "for p in ld.patches:\n", " _x = p.get_x() + p.get_width()\n", " _y = p.get_y() + p.get_height() - 0.15\n", " percent = round(((p.get_width() / 22561) * 100), 1)\n", " string = str(int(p.get_width()))\n", " if len(string) == 5:\n", " value = string[:2] + ',' + string[2:]\n", " elif len(string) == 4:\n", " value = string[0] + ',' + string[1:]\n", " else:\n", " value = string\n", " value += ' ({})'.format(str(percent)+'%')\n", " ld.text(_x + 150, _y, value, ha='left', weight='bold')\n", "\n", "fig.savefig(os.path.join('vis', 'OJS_primary_languages.png'), bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar plot of all the languages in which OJS users published their articles (*n*=22,561): " ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "fig, ax = matplotlib.pyplot.subplots(figsize=(8,16))\n", "\n", "mult = sns.barplot(y=list(ls.sort_values().index),\n", " x=list(ls.sort_values().values),\n", " orient='h',\n", " color='grey')\n", "\n", "sns.despine(bottom=True)\n", "\n", "ax.set(xlim=(0, 12500),\n", " xlabel='Active journals using OJS',\n", " ylabel='Language',\n", " title='Primary language employed by journals using OJS ($\\it{n}$ = 22,561)',\n", " visible=True)\n", "\n", "matplotlib.pyplot.xticks([0, 2000, 4000, 6000, 8000, 10000, 12000],\n", " ['0', '2,000', '4,000', '6,000', '8,000', '10,000', '12,000'])\n", "\n", "for p in mult.patches:\n", " _x = p.get_x() + p.get_width()\n", " _y = p.get_y() + p.get_height() - 0.175\n", " percent = round(((p.get_width() / 22561) * 100), 2)\n", " string = str(int(p.get_width()))\n", " if len(string) == 5:\n", " value = string[:2] + ',' + string[2:]\n", " elif len(string) == 4:\n", " value = string[0] + ',' + string[1:]\n", " else:\n", " value = string\n", " value += ' ({})'.format(str(percent)+'%')\n", " mult.text(_x + 125, _y, value, ha='left', weight='bold')\n", "\n", "fig.savefig(os.path.join('vis', 'OJS_languages_v3.png'), bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar plot of multilingualism among journals publishing with OJS (*n*=22,382)
\n", "Each bar represents the proportion of journals that published **5 or more articles in each of their publishing languages**. The decision boundary of 5 was chosen to match the decision boundary for active journals (>=5 articles published per year)." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "def classify_journals_multi(issn2langs, decision_boundary):\n", " multilingual = defaultdict(list)\n", " for k, v in issn2langs.items():\n", " for lang in Counter(v).items():\n", " if lang[1] >= decision_boundary: \n", " #If the the number of article abstracts tagged as a given language ('en') exceeds the boundary\n", " multilingual[k].append(lang[0]) #Append the language to a list for the journal\n", " \n", " multilingual_counts = defaultdict(int)\n", " array_lengths = []\n", " for v in multilingual.values():\n", " if v:\n", " multiplier = len(v)\n", " array_lengths.append(multiplier)\n", " if multiplier >= 3:\n", " multilingual_counts['Multi- (3+ languages)'] += 1\n", " elif multiplier == 2:\n", " multilingual_counts['Bi- (2 languages)'] += 1\n", " elif multiplier == 1:\n", " multilingual_counts['Mono- (1 language)'] += 1\n", " else:\n", " continue\n", " \n", " total = 0\n", " for v in multilingual_counts.values():\n", " total += v\n", " print('Total: {} journals'.format(total))\n", " print('Average number of languages per journal: {}'.format(np.array(array_lengths).mean()))\n", " \n", " return pd.Series(multilingual_counts).sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total: 22382 journals\n", "Average number of languages per journal: 1.6994013046197838\n" ] } ], "source": [ "multi5 = classify_journals_multi(issn2langs, decision_boundary=5)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "fig, ax = matplotlib.pyplot.subplots()\n", "\n", "mult = sns.barplot(y=multi5.index,\n", " x=multi5.values,\n", " orient='h',\n", " color='grey')\n", "\n", "ax.set(xlim=(0, 12000),\n", " xlabel='Active journals using OJS',\n", " ylabel='*-lingual journals')#,\n", " #title='Number of languages employed by journals using OJS ($\\it{n}$ = 22,382)')\n", "\n", "sns.despine(bottom=True)\n", "\n", "matplotlib.pyplot.xticks([2000, 4000, 6000, 8000, 10000],\n", " ['2,000', '4,000', '6,000', '8,000', '10,000'])\n", "\n", "for p in mult.patches:\n", " _x = p.get_x() + p.get_width()\n", " _y = p.get_y() + p.get_height() - 0.35\n", " percent = round(((p.get_width() / 22382) * 100), 1)\n", " string = str(int(p.get_width()))\n", " if len(string) == 5:\n", " value = string[:2] + ',' + string[2:]\n", " elif len(string) == 4:\n", " value = string[0] + ',' + string[1:]\n", " else:\n", " value = string\n", " value += ' ({})'.format(str(percent)+'%')\n", " mult.text(_x + 150, _y, value, ha='left', weight='bold')\n", "\n", "fig.savefig(os.path.join('vis', 'OJS_multilingual5.png'), bbox_inches=('tight'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 4 }