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Record W3123431171 · doi:10.3386/w16846

Industrial Actions in Schools: Strikes and Student Achievement

2011· preprint· en· W3123431171 on OpenAlexafffundabout
Michael Baker

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typepreprint
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaDalhousie UniversityMcMaster University
KeywordsMathematics educationStudent achievementPsychologyPedagogyAcademic achievement

Abstract

fetched live from OpenAlex

While many jurisdictions ban teacher strikes on the assumption that they harm students, there is surprisingly little research on this question. The majority of existing studies make cross section comparisons of students who do or do not experience a strike, and report that strikes do not affect student performance. I present new estimates from a sample of strikes in the Canadian province of Ontario over the period 1998-2005. The empirical strategy controls for fixed student characteristics at the school cohort level. The results indicate that teacher strikes in grades 2 or 3 have on average a small, negative and statistically insignificant effect on grade 3 through grade 6 test score growth, although there is some heterogeneity across school boards. The effect of strikes in grades 5 and 6 on grade 3 through grade 6 score growth is negative, much larger and statistically significant. The largest impact is on math scores: 29 percent of the standard deviation of test scores across school/grade cohorts.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.833

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.804
GPT teacher head0.643
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes3
Has abstractyes

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