Social Status of Researchers And Professional Practices in the Field of Research Aimed at Social Intervention in France
Bibliographic record
Abstract
During a governmental mission to France in March of 1988 the author evaluated the effects of the status and professional practices of researchers on their work in the field of ‘social research’. This type of research is largely financed by sectorial ministeries and private associations formed under the provisions of the law of 1901. I examined the sociopolitical and scientific milieu of knowledge production in this field of study. Such research on social aspects of health, social problems and income security raises fundemental epistemological questions regarding its legitimacy, specificity, scientific validity, and applicability. A traditional theoretical orientation in social science research, lack of interest in funding this type of research at the CNRS (Centre National de la Recherche scientifique) and scientific ministries, and the precarious status of young researchers all characterize this new field of study and also constrain its development. However a new momentum is under way, due to recent initiatives by the Mission de Recherche et d’Experimentation (MIRE), the Ministère des affaires sociales et de l’emploi, and to political lobbying by public bodies and private organizations with the goal of revitalizing social research aimed at finding concrete and innovative solutions to contemporary problems through the application of research.
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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 | 0.080 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".