Research in Arabic-speaking countries: Funding competitions, international collaboration, and career incentives
Bibliographic record
Abstract
Morocco, Tunisia, Egypt, Lebanon, Jordan, and Qatar expanded research funds over the past two decades. The use of competitive calls required researchers to prepare and submit proposals for team-based projects or time-limited research units. Identification of national priorities and societal challenges sought to rally research toward real-world problems, while larger grants encouraged a wider range of research activities and greater levels of ambition. Yet, the incentives within hiring organizations still determine how researchers allocate their time and effort, including whether they even seek external funding or collaboration. Selection and evaluation criteria privileged collaboration with distant, scientifically proficient partners abroad, in order to connect with global networks and rise in international rankings of academic quality. Moving forward, countries need to consider how funding opportunities shape the size and organization of distinct research efforts, and which arrangements are best suited to making meaningful progress on different problems of societal and scientific interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".