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Record W7132915333

The Cost of Failure in Ontario's Public Secondary Schools

2013· dissertation· en· W7132915333 on OpenAlexaffabout
Brent Faubert

Bibliographic record

VenueTSpace · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsCanadian Association for the Study of Adult Education
Fundersnot available
KeywordsSecondary educationWork (physics)Resource (disambiguation)Order (exchange)Cost effectivenessOpportunity costEmpirical evidence
DOInot available

Abstract

fetched live from OpenAlex

Large bodies of empirical evidence show that policies and practices that support failure in schools does little to improve student outcomes, yet course failure remains widespread in secondary schools. Further, there is a growing body of evidence indicating these policies and practices are costly in fiscal terms. This study builds on this body of evidence to ask the question: how much money does course failure in secondary schools cost the Ontario public education system annually? Borrowing from Levin & McEwan’s resource cost modelling approach, the study calculates the volume of course failure across all secondary schools in the province and establishes estimates of the annual cost of secondary course failure taking into account some factors known to be systematically related. This work aims to better understand the costs of providing public secondary school education in order to make more effective use of resources. In the 2008–09 school year 7.9% of course registrations in Ontario secondary schools resulted in failure. Fail rates are greater for students who receive special services and vary considerably by subject area. The annual cost is estimated to be $472 million.

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)0.007
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 score0.071
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.370
Teacher spread0.325 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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