Bibliographic record
Abstract
Teacher strikes can have many of effects, including creating major inconveniences for parents, but the most contentious issue is undoubtedly the impact on student learning. The argument against teachers’ right to strike is based on the assumption that teacher strikes negatively affect student achievement. However, there is surprisingly little research on the impact of teacher strikes on student development. The few existing studies typically make point in time comparisons of the achievement of students who do or do not experience a strike –an empirical strategy that is unlikely to reveal whether teacher strikes affect students. These studies tend to conclude that strikes do not have a significant impact. A study by CLSRN affiliate Michael Baker (University of Toronto) entitled “Industrial Actions in Schools: Strikes and Student Achievement†(CLSRN Working Paper no. 111) employs a different empirical strategy which compares the achievement of student cohorts before and after a strike. He finds that the effect of longer strikes is negative and significant for critical subjects such as reading and mathematics. The discipline of Economics is generally classified as a “social science†and thus the perception exists that the post-graduation earnings of an Economics degree graduate would be closer to that of other social science graduates, rather than wages of traditionally higher-paying disciplines in business, engineering, law or the natural sciences. While enrolment in Economic disciplines has been on an increasing trend in most Western countries since the Great Recession of 2007, this same trend has not been observed in Canada. A potential reason for this outcome may be the continued perception of Economics as a social science with the associated less lucrative wage outcomes compared to disciplines such as business, engineering and the natural sciences – which are traditionally perceived as high-paying disciplines. A study entitled “Economic Benefits of Studying Economics in Canada: a Comparison of Wages of Economics Majors with those in Other Disciplines Circa 2005†(CLSRN Working Paper no. 109) by CLSRN affiliates Ather H. Akbari and Yigit Aydede (both of St. Mary’s University) examines the wages earned by university degree holders in 50 disciplines in relation to economics, and finds that graduates in Economics actually earn higher wages than most other disciplines – significantly more than graduates of other social science disciplines such as Political science.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.542 | 0.414 |
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".