Les conclusions formulées par Boyer et Bissonnette en 2021 sont valables en 2023 : une réponse au texte de Allaire et ses coll``egues (2022)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it