Bibliographic record
Abstract
The minimal-marking project conducted in Ryerson’s School of Journalism throughout 2012 and early 2013 resulted in significantly higher grammar scores in two first-year classes of minimally marked university students when compared to two traditionally marked classes. The “minimal-marking” concept (Haswell, 1983), which requires dramatically more student engagement, resulted in more successful learning outcomes for surface-level knowledge acquisition than the more traditional approach of “teacher-corrects-all.” Results suggest it would be effective, not just for grammar, punctuation, and word usage, the objective here, but for any material that requires rote-memory learning, such as the Associated Press or Canadian Press style rules used by news publications across North America. Le projet de corrections minimales mené à l’École de journalisme de Ryerson tout au long de 2012 et au début de 2013 a eu pour résultat des notes de grammaire considérablement supérieures dans deux classes de première année d’étudiants universitaires corrigés de façon minimale par rapport à deux classes où les étudiants étaient corrigés de façon traditionnelle. Le concept de « corrections minimales » (Haswell, 1983), qui exige un engagement considérablement plus important de la part des étudiants, aboutit à des résultats d’apprentissage supérieurs en ce qui concerne l’acquisition de connaissances au niveau superficiel par rapport à l’approche traditionnelle du « professeur qui corrige tout ». Les résultats suggèrent que cette approche serait efficace, non seulement pour la grammaire, la ponctuation et le bon usage des mots, qui étaient l’objectif visé dans ce cas, mais également pour n’importe quelle matière qui exige un apprentissage par mémorisation, tel que les règles de style de la Associated Press ou de la Presse canadienne utilisées par les publications de presse d’un bout à l’autre du Canada.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".