Progressive until graduation? Helping BSW students hold onto anti-oppressive and critical social work practices
Bibliographic record
Abstract
Former BSW student: I’m really worried about this job interview. I know they are going to think I am too critical, too passionate, too much. How can I dumb myself down Jennifer? How do I get in the door so I can do the work I want to do? Maybe staying quiet will get me the job I need…maybe I should shut up about AOP? This was part of a conversation I had last week, with a passionate, anti-oppressive and critical former undergraduate student who had been told, on more than one occasion, that she was just “too much” for the ‘mainstream’ social work organizations to which she had been applying for employment. It was not the first time one of my graduates had shared such worries, for many had reported negative workplace reactions to their critical and anti-oppressive stance, nor would it be the last. As the literature reminds us, social workers are now labouring in a post-welfare context where critiques of power, racism, ageism, sexism, heterosexism and ableism will not make ‘best practice’ lists unless they also save money and increase productivity (Baines, 2007; Hugman, 2001). As Donna Baines writes,
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 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".