Localization of Social Work Knowledge through Practitioner Adaptations
Bibliographic record
Abstract
ABSTRACT. Social work is only just beginning to adapt knowledge and practice to the realities of a geographically diverse world. Within the social services, one of the most exciting diversity-related initiatives is a localization movement that calls for a social work knowledge base that is fundamentally different from one geographic milieu to the next. Few, if any, studies to date have considered the Canadian North (an area populated by diverse aboriginal cultural and linguistic groups) as a basis for localizing social work knowledge. This study reports on interviews conducted with social work practitioners in northern Ontario and the Northwest Territories to gain insight into how changes in the current social work knowledge base could be the locus for meaningful and contextually sensitive social work knowledge and intervention. This initial exploratory study presents a number of key findings that aid in developing an understanding of social work practice and knowledge specific to the Canadian North. These findings identify geographical areas where social work knowledge requires adaptation, changes in the personal and professional behaviour of practitioners, or modification of mainstream knowledge; use of appropriate and inappropriate social work theory and practice; specific challenges faced by agencies; ways agencies can modify programs to meet community needs; ways for clients to access service; and the relationships between practitioners and the surrounding communities. We conclude with implications for the Canadian North related to social work, allied disciplines, and social welfare structures. Key words: social work practice, northern Canada, localization, social work knowledge RÉSUMÉ. Le travail social ne fait que commencer à adapter les connaissances et les pratiques aux réalités d’un monde
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".