Language of citation and publishing performance of graduate students in French-speaking countries with different economic and linguistic advantages
Bibliographic record
Abstract
The performance of graduate students in research varies greatly across countries due to various factors, mainly socioeconomic and linguistic. The current situation is critical because the wealthiest countries are also the most linguistically equipped to navigate the English-dominant landscape of academia. Here, we assess the language of citations and the publishing performance of graduate students from three French-speaking countries: Algeria, Canada, and France, where Algeria is the least English proficient and the most economically disadvantaged. We found that the bibliography of PhD theses were English dominated in all regions (72.5% in Algeria compared with >93.1% in Western countries), whereas those of Masters theses were French dominated in Algeria (63.3%), relatively bilingual in France (47.6% French), but English dominated in Canada-Québec (94.7%) and Canada-BC (98.7%). Algerian PhD students produced fewer papers, were less likely to publish in journals with calculated impact factors, and received fewer citations than students who graduated from universities in France or in two Canadian provinces, British Columbia and Québec. Our results suggest that the economic and linguistic disadvantages faced by graduate students from non-Western backgrounds affect their academic performance, highlighting important issues in facing future global challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".