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Record W7132893753

'Safe' Schools: Safe for Who?: Latinas, 'Thugs', and Other Deviant Bodies

2009· dissertation· en· W7132893753 on OpenAlexaffabout
Paulina A. Vivanco

Bibliographic record

VenueTSpace · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEvictionNarrativePower (physics)Work (physics)Function (biology)Space (punctuation)Identity (music)
DOInot available

Abstract

fetched live from OpenAlex

This analysis is concerned with the spatially-anchored hierarchies of power that organize Ontario’s current schooling model. Using the experiences of four young Latina girls, it questions how current school safety discourses function as barriers to educational success, vis-à-vis their role in reconfiguring these students’ identities through narratives of danger, menace, and unruliness. Specific safety and security related practices are explored as sites through which marginalized students are produced as dangerous bodies who are undeserving of full educational opportunities. It is argued that these practices (as manifest in current approaches to surveillance, policing, discipline and punishment, and the restriction of educational mobility) all work to produce the school space as dominant space. Rather than offering youth the opportunity to overcome inequalities, schools and education instead play a definitive role in their continued propagation by sanctioning the control, containment, and eviction of those who are deemed to be deviant.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.925

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.011
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.074
GPT teacher head0.492
Teacher spread0.418 · 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 designQualitative
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
Published2009
Admission routes2
Has abstractyes

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