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Record W2787687859 · doi:10.5430/wje.v8n1p1

The Impact of Zero Tolerance Policy on Children with Disabilities

2018· article· en· W2787687859 on OpenAlexvenueno aff
Mariam Alnaim

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsZero tolerancePsychologyLearning disabilitySAFERAction (physics)DisciplineZero (linguistics)InstitutionMedical educationMathematics educationApplied psychologyPedagogyPublic relationsPolitical scienceDevelopmental psychologyMedicineComputer securityCriminologyComputer scienceLaw

Abstract

fetched live from OpenAlex

The Zero Tolerance policy was intended to eliminate learners who are a danger to a learning institution (Henson,2012). The development of this policy was to assist schools with better policing approaches of students conducts byemploying tough disciplinary action and subsequently provide a safer learning environment. While the ZeroTolerance policy sought to reinforce security measures in schools, the students with emotional or learning disabilitiesand behavioral disorders were predisposed to expulsions and suspensions (Henson, 2012). The situation is facilitatedby the all-encompassing nature of this policy as it fails to accommodate the fact that some of the behaviorsdemonstrated by students with disabilities are beyond their control. While some of these behaviors are considered tofall under the zero-tolerance policy guidelines, it subjects this group of learners several disciplinary actions that werenot initially included in addressing their special needs.

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)0.004
metaresearch head score (Gemma)0.024
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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.000

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 head0.032
GPT teacher head0.423
Teacher spread0.391 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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