Jennifer Sclater, was an ICT Consultant at the Centre for the Study of Learning and Performance, Concordia University, Montreal, Quebec, and is currently Coordinator, Education (youth programs) with the World Anti-Doping Agency, Montreal, Quebec.
Bibliographic record
Abstract
Abstract: The current investigation was an exploration of the first year of a multi-year project designed to provide every Grade 3 to Grade 11 student throughout an English school board in Quebec with a laptop computer. Data were collected from 403 elementary and 270 secondary students from the experimental school board and also from 330 students in the control school board. In addition, questionnaire data were collected from 60 elementary school teachers and 51 secondary school teachers. Finally, interviews were conducted with 72 students and 20 teachers. Potentially the most interesting finding was the difference in achievement scores between the experimental and control boards. Secondary students from the experimental board had higher scores on the CAT-3 reading test and indicated making six times more frequent use of computer technology in their English classes, suggesting a possible treatment effect. In contrast, math scores were higher at the control board where neither board indicated high levels of computer use. Nevertheless, these findings must be interpreted with some caution until the threats to validity of selection bias are more clearly overcome.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.010 |
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".