Bibliographic record
Abstract
The profession of social work in Canada has been constructed historically as 'female ' due to the gendered social expectations of the caring and nurturing roles. Men, as a result, are perennially in a minority position both within the classroom and within the profession as a whole (Baines et al, 1998). Men within social work are faced with defining their competency within the field in a manner that may challenge socially sanctioned understandings of masculinity. If men do not challenge stereotypic roles of masculinity, they run the risk of replicating troubling gender relations so that the male remains dominant even in a female defined profession(Baines et. al., 1998; Berger et. al., 1995). At the same time, the populations served by social workers have become increasingly diverse by sexual orientation, ethnicity, and race. The over representation of women and the poor on social work caseloads has required that social workers be sensitive to matters of class and gender. A contemporaneous development in Toronto, Canada has been the increasing diversity within the social work classroom. The classroom has become a site of multiple possibilities due to the diversity of the student population (Razack, 1999; Rossiter, 1996). Central to the task of multicultural learning and teaching is the ability of both the social work students and professor to reflect critically on their socially defined roles ( Fook, 1999; Moffatt &Miehls, 1999; Rossiter, 1996). Social work educators have a complex task; they endeavour to teach social work practice while facilitating respectful communication across difference within the classroom (Moffatt, 1996; Moffatt & Miehls,
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.919 | 0.823 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".