Bibliographic record
Abstract
Pour saisir les stratégies déployées par les Sahéliennes face au déficit vivrier, il faut d’abord comprendre pourquoi les femmes créent leurs propres stratégies et à partir de quelle situation elles les développent. La non-mixité des sociétés africaines et le patriarcat sont des données indispensables pour saisir pourquoi ces paysannes possèdent des stratégies différentes de celles des hommes. L’étude des différents facteurs explicatifs du déficit vivrier facilite la compréhension de la situation dans laquelle vivent ces paysannes et surtout de ce par rapport à quoi elles réagissent. L’analyse des stratégies élaborées par les Sahéliennes se divise en deux parties : les stratégies individuelles et les stratégies collectives, c’est-à-dire celles qui concernent les associations formelles ou informelles de paysannes. La conclusion porte sur la question suivante : les Sahéliennes sont-elles porteuses de pratiques alternatives de développement ?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".