Success in Writing and Attributions of 16-Year-Old French-Speaking Students in Minority and Majority Environments
Bibliographic record
Abstract
This article examines causal attributions of writing performance made by 16-year-old French-speaking Canadian students (N=3,874). The students are from the French-speaking majority province (Quebec) and minority provinces in Canada (Manitoba, Ontario, New Brunswick, and Nova Scotia). The data came from the School Achievement Indicators Program (SAIP) Writing Assessment III (Council of Ministers of Education, 2002). A total of 15 variables are related to causal attributions of failure and success in writing. The interaction between these variables and the type of environment (i.e., minority vs. majority French environments) indicated that French-speaking students in a minority environment did not perform as well as those from a majority linguistic environment because they did not study hard enough, the teacher marked too severely, they had bad luck, and the course was not well taught. When they were successful, it was because they studied hard at home and attributed their good marks to working hard enough, the teacher being lenient marking, and having good luck. The majority group attributed their good marks to the ease of the course and their bad marks to its difficulty.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".