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Record W7101386857 · doi:10.18061/dsq.6871

Contending with the “adult gaze” in Mad Studies: Participatory research methods led by psychiatrized children and youth

2025· article· W7101386857 on OpenAlexaff

Bibliographic record

VenueDisability Studies Quarterly · 2025
Typearticle
Language
FieldArts and Humanities
TopicCultural History and Identity Formation
Canadian institutionsYork University
Fundersnot available
KeywordsParticipatory action researchPower (physics)Citizen journalismField (mathematics)Mental healthAction (physics)

Abstract

fetched live from OpenAlex

Drawing on mad studies as a field of scholarship, critical and feminist perspectives, and my experiences as a participatory action researcher, I consider the ethical and epistemological tensions associated with conducting research with children and youth positioned as “mental health service-users”. Specifically, I discuss the tensions associated with the “adult gaze”, our power and authority as adults over children and the ways in which “being an adult” influences and organizes the interpretations, and subsequent representations we can make of children and their lives. Essentially, I bring into question unexamined assumptions that engagement, inclusion, and participation are enough to result in an anti-oppressive shift in institutional power from adults to children given the complexity of adults’ social power over children and their lives. I conclude by proposing strategies for moving from participatory to child-led/youth-led research, that is, for the development of mental health theories and practices led by young people’s epistemologies – their knowledges and ways of knowing.

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)0.121
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0210.052
Scholarly communication0.0150.010
Open science0.0040.026
Research integrity0.0030.007
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 head0.181
GPT teacher head0.445
Teacher spread0.264 · 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.

Study designQualitative
DomainMethods
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
Published2025
Admission routes1
Has abstractyes

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