Social Work Practice, Data, Time and Biopolitics: Looking at Short-Term RFPs in Contemporary Community Practice in a Canadian Context
Bibliographic record
Abstract
My study uses Discourse Analysis (Garrity, 2007; Jäger, 2004) to read short-term Request for Proposals (RFPs) to study the relationship between linear temporality, community work and how governments produce vulnerable subjects through data.In particular, I am interested in the ways that linear time serves as a backdrop facilitating the datafication (Dijk, 2014) of practice, and the shift from the relational to the informational in social work practice (Parton, 2008).To get a sense of how linear time and the use of data is potentially implicated in the construction of knowable and governable subjects, I utilize a conceptual framework of Biopower (Cruikshank, 1999;Million, 2013).This study hopes to contribute to relevant social work and communications literatures by focusing on data, subjectivity, linear time as a tool of social control and contemporary social work practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.038 | 0.079 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".