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Record W2147301928 · doi:10.1093/bjsw/bct139

Performativity Culture in Universities: Social Work Fabrications

2013· article· en· W2147301928 on OpenAlexaffabout
Sarah Todd, Lisa Barnoff, Ken Moffatt, Melanie Panitch, Henry Parada, Mandeep Kaur Mucina, Dylan Williams

Bibliographic record

VenueThe British Journal of Social Work · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsPerformativitySociologySocial workWork (physics)Space (punctuation)Diversity (politics)Social spacePedagogyPublic relationsMedia studiesAestheticsGender studiesPolitical scienceEngineeringAnthropologyArtMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

This paper examines the findings of a study we completed into progressive social work education in Canadian schools of social work. In our research, we found that schools of social work offer three key functions for the university: a connection to the community, a space that values diversity and a space of innovation. We investigate these themes in relation to the dynamics of performativity and fabrication. From conversations with those who participated in our research, it is clear that schools of social work play a key role in the university in terms of constructing a particular institutional image—a fabricated image. We articulate some key aspects of performance that are drawn upon in fabricating contemporary Canadian universities and the ways in which social work is positioned to enhance this fabrication. In the conclusion, we reflect upon what these findings and analysis mean for social work education.

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)0.016
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0440.092
Scholarly communication0.0190.006
Open science0.0020.016
Research integrity0.0020.004
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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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
DomainIncentives
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

Citations18
Published2013
Admission routes2
Has abstractyes

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