French-Language Public Administration Research on Social Equity: A Systematic Literature Review
Bibliographic record
Abstract
Despite the availability of freely accessible translation tools, research conducted in languages other than English is often overlooked. Findings from foreign contexts are lost, undermining the boundary conditions of theories. This systematic literature review takes stock of methods, theories, and practical recommendations developed in French-language social equity research. Overall, the results suggest that French-language social equity research offers relevant but not in-depth practical recommendations, encompasses a small proportion of papers referring to theories, and is more qualitative method-oriented than quantitative. Our results complement previous findings suggesting the dominance of the quantitative approach to social equity research by bringing to light many qualitative studies. Crucially, unlike English-language social equity research, this study suggests that French-language social equity research rarely focuses on race and ethnicity. Inter-organizational equity, along with regional, intergenerational, and gender equity are frequent loci. The finding of this study bridges a heeded segment of Public Administration scholarship, fostering knowledge sharing across languages and scholarly communities.
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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 | 0.037 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".