COVID-19 et offre de cours en ligne au Niger : prospection sur les raisons d'un échec
Bibliographic record
Abstract
Pour faire face à la pandémie de COVID-19 à l'échelle mondiale, les mesures de confinement et de fermeture des écoles et universités ont engendré l'usage de formes diversifiées d'enseignement à distance à grande échelle afin d'assurer une continuité pédagogique dans des conditions inédites et improvisées.Au Niger, le ministère de l'Enseignement supérieur propose une initiative comme solution alternative : organiser les enseignements sur les réseaux sociaux WhatsApp et Telegram.Ces offres n'ont permis à aucune activité de voir le jour jusqu'à la réouverture des écoles et universités le 1 er juin 2020.Le présent article analyse les conditions de l'offre et les raisons de l'échec afin de tirer les leçons qui s'imposent et d'anticiper des offres porteuses dans les crises futures.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".