Les conclusions formulées par Boyer et Bissonnette en 2021 sont valables en 2023 : une réponse au texte de Allaire et ses coll``egues (2022)
Bibliographic record
Abstract
Boyer and Bissonnette (2021) published a synthesis of research that measured the effects of virtual school on student achievement before and during the COVID-19 pandemic. The researchers showed that fully online schools generally produce significantly lower learning gains than brick-and-mortar schools. Following this publication, Allaire et al. (2022) reanalyzed some of this research and present different arguments attempting to mitigate the ineffectiveness of virtual schools. In this article, we take up each of these arguments and demonstrate that they are flawed, invalid, and unauthorized. Therefore, we consider the conclusions made by Boyer and Bissonnette in 2021 to be just as valid and tenable in 2023!
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 | 0.047 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".