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Record W4211126178 · doi:10.32920/ryerson.14655984

Broken Record: An Arts-Informed Autoethnography of Adolescent Institutionalization

2021· preprint· en· W4211126178 on OpenAlexaff
Alison Aird

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsAutoethnographyContext (archaeology)InstitutionPresentation (obstetrics)InstitutionalisationNarrativeSubjectivityIdentity (music)SociologyPsychologyThe artsMeaning (existential)PedagogyVisual artsAestheticsPsychotherapistEpistemologySocial scienceHistoryArtMedicineLiteraturePsychiatry

Abstract

fetched live from OpenAlex

“Broken Record” is a Masters Research Project in which I explore my experience in an adolescent psychiatric institution using an arts-informed autoethnographic method. The final project is a 200-page artistic exploration of language, meaning, identity, and psychiatry. This component of the research outlines the critical objectives of the project and grounds the work in a body of existing literature. The primary contribution of the paper is its presentation of Madness as Method, a distinct approach to autoethnographic research on madness and psychiatric survival that mobilizes mad subjectivity to generate knowledge from a place of embodiment, distress, memory work, and academic research. I outline this methodology at length, identifying and exploring its four stages: unravelling, integration, narrative, and reckoning. I conclude this paper by situating my Masters Research Project in the context of my Masters training and my professional goals beyond the academy.

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)0.011
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.113
GPT teacher head0.331
Teacher spread0.219 · 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
Published2021
Admission routes1
Has abstractyes

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