« Ces lois qui brouillent les races ». Enseignement, ségrégation et égalité en Alabama et au Tennessee (1865-1899)
Bibliographic record
Abstract
Le présent article examine l'intervention des assemblées législatives de l'Alabama et du Tennessee en matière d'enseignement, de l'Émanicipation jusqu'au début du XX e siècle. L'objectif est d'exposer le processus par lequel les élus de ces États sudistes munirent les remparts de la ségrégation de balistes destructeurs d'égalité des chances. En précisant l'acception du rapport entre la ségrégation et l'égalité, l'article révèle que les objectifs les plus précieux du système Jim Crow se trouvaient dans les lois scolaires définissant le pouvoir d'imposition et la distribution des taxes. Portée à la lumière du temps présent, cette histoire rend intelligible l'état du projet d'égalité depuis l'abolition de la ségrégation aux États-Unis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".