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Record W7126351474

Capped – closing off the School to Prison Pipeline: an anti-racism workshop for educators to reduce suspensions of African Canadian learners

2024· other· en· W7126351474 on OpenAlexaboutno aff
Marcia-Lisa Charmain Dennis

Bibliographic record

VenueOpenBU (Boston University) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRacismPrisonUnconscious mindRacial biasSchool disciplineCriminal justiceDisciplineEconomic JusticeClosing (real estate)
DOInot available

Abstract

fetched live from OpenAlex

Systemic institutionalized racism in the school system is manifested by teachers’ biases due to unconscious or conscious white supremist beliefs, attitudes, and behaviours which has impacted academic achievement of African Canadian (AC) learners in preschool, elementary, and secondary school. AC learners struggle at a disproportionate rate with the consequences of zero tolerance policies in the school system leading to practices of exclusionary discipline, also known as detention, suspension, or disciplinary alternative education placements which normalize prison. This is a precursor to the criminal justice system that leads to incarceration which is referred to as the School to Prison Pipeline (STPP). A two-day anti-black racism workshop will address unconscious teacher bias in a safe space and help them build skills of cultural competency, cultural humility, and culturally responsive teaching. This program is supported by evidence-based literature, and the theoretical frameworks of Critical Race Theory, the Stages of Change Model and the Canadian Model of Occupational Performance and Engagement. Various teaching methods will be used to engage the participant to reflect on their bias and an evaluation, funding, and dissemination plans are described.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.574
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.002
Scholarly communication0.0020.001
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.001

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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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