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Record W4387740974 · doi:10.3102/00346543231203674

Exposure to Adversity and Trauma Among Students Who Experience School Discipline: A Scoping Review

2023· review· en· W4387740974 on OpenAlexafffund
Jane E. Sanders, Andrea Joseph-McCatty, Michael Massey, Emma Swiatek, Ben Csiernik, Elo Igor

Bibliographic record

VenueReview of Educational Research · 2023
Typereview
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsOntario Tech UniversityThe King's University
FundersKing's University College
KeywordsPsychologyRacismPovertySchool disciplineClinical psychologyPedagogySociologyPolitical scienceGender studies

Abstract

fetched live from OpenAlex

While the disproportional application of school discipline has garnered notable attention, the relationship between trauma or adversity and school discipline is under examined. The purpose of the current scoping review was to map the state of the literature, empirical and theoretical, at the intersection of school discipline, and trauma or adversity. The findings identified a gap in our knowledge as only 14 of the 49 included articles detailed empirical studies focused on the relationship between adversity and school discipline, with very few from outside of the United States. However, this burgeoning body of knowledge points to a significant relationship between trauma/adversity and experiencing school discipline that warrants further study and contextualizes expanded adversities, including poverty and racism as adversity. We believe this is necessary to acknowledging the hidden and unaddressed trauma among students being disproportionally disciplined, leading to a greater understanding of student lives, and evidence-based, trauma-informed, and culturally attuned discipline.

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)0.006
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.405
GPT teacher head0.649
Teacher spread0.244 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations20
Published2023
Admission routes2
Has abstractyes

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