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Record W2169372421 · doi:10.1080/14681366.2014.919956

How to ‘fail’ in school without really trying: queering pathways to success

2014· article· en· W2169372421 on OpenAlexaff
Sam Stiegler, Rachael Sullivan

Bibliographic record

VenuePedagogy Culture and Society · 2014
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQueerNormativeNegotiationSociologyPedagogyCareer PathwaysGender studiesPsychologyMathematics educationPolitical scienceMedical educationSocial scienceMedicine

Abstract

fetched live from OpenAlex

In this paper we explore our experiences working with queer and trans youth who have taken ‘non-traditional’ pathways out of high school. Drawing on Foucauldian theories of normalisation and Halberstam’s queerings of time, success, and failure, we consider how certain aspects of schooling have shaped queer and trans youths’ desire to seek out GEDs or early entrance into post-secondary school as strategies to escapes their high school environments. Through our reflection on our experiences with these students, we have identified some of the barriers that these students faced with regard to high school completion. Additionally, we found the tension between success and failure often shaped the alternative paths these queer and trans youth had chosen in an effort to negotiate their schooling experience. In the end, we question the current organisation of secondary schooling and suggest that a (re)envisioning of alternative educational pathways out of secondary schooling would provide destigmatising, non-normative modes of engagement with schools and learning.

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)0.008
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0210.047
Scholarly communication0.0120.011
Open science0.0020.012
Research integrity0.0020.007
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 head0.034
GPT teacher head0.370
Teacher spread0.336 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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