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Record W2619633496 · doi:10.3102/0002831217705060

Are Charter Schools Safer in Deindustrialized Cities With High Rates of Crime? Testing Hypotheses in Detroit

2017· article· en· W2619633496 on OpenAlexaff
Daniel Hamlin

Bibliographic record

VenueAmerican Educational Research Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCharterSAFERCharter schoolSchool choicePolitical sciencePsychologyComputer security

Abstract

fetched live from OpenAlex

Families in deindustrialized cities with high crime rates report prioritizing school safety when opting for charter schools. Yet, very little research has investigated whether charter schools are safer than traditional public schools. This study compares charter and traditional public schools in Detroit, Michigan, on perceived school safety by linking student surveys to data on school, neighborhood, and parent-related characteristics. Charter schools exhibited higher perceived school safety than traditional public schools. However, controls for student commute distance and parental involvement largely diminished this difference. Neighborhood charter schools were an exception, maintaining higher perceived school safety net of controls. Overall results suggest that differences in perceived school safety between schools become less prominent after the attributes of school choosers are considered.

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)0.003
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 score0.220
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.402
GPT teacher head0.528
Teacher spread0.126 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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