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Record W4407346397 · doi:10.1111/ijsw.12720

Behind closed doors: Sousveillance in mandated social welfare interventions

2025· article· en· W4407346397 on OpenAlexafffund
Tara La Rose, Jennifer Mulé

Bibliographic record

VenueInternational Journal of Social Welfare · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDoorsPsychological interventionWelfareSocial WelfareSocial workBusinessPublic economicsNursingMedicineEconomicsPolitical scienceComputer scienceEconomic growthLawMarket economy

Abstract

fetched live from OpenAlex

Abstract The Behind Closed Doors project is a qualitative research study considering sousveillance in mandated social welfare contexts. Sousveillance (the practice of recording people in authority without their consent) and sousveillance social media advocacy (posting sousveillance recordings publicly without consent) are significant phenomena effecting the delivery of contemporary mandated social services (e.g., child protective services; probation and parole; mandatory mental health intervention). Sousveillance video recordings shared online via social media (e.g., YouTube and TikTok) are analysed using multi‐modal discourse analysis; individual interviews with client content creators, frontline social workers, union representatives and professional regulators supported a deeper understanding of the effect of sousveillance on workers, clients and organisations. The significance of sousveillance as a resource for shifting power relations in practice is also considered.

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)0.027
metaresearch head score (Gemma)0.040
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 score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0040.004
Open science0.0020.011
Research integrity0.0020.002
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 head0.036
GPT teacher head0.455
Teacher spread0.419 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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