Bibliographic record
Abstract
Employing critical autoethnography, this article conveys how over my four decades of social work, I have come to adopt a contextualized social work stance and identifies what emerge as four key areas of contextualized social work. These include attention to race, ethnicity and culture as experienced in the local environment, the local articulation of social conditions and appropriate social work responses, the activation of local knowledge generation and curation, and finally, addressing and resisting expert power. Such theorization of contextualized social work augments previous work that positions contextualized social work as countering dominant conceptualizations of social work and instead centering on a critical interrogation of the local, foregrounding local understandings of social conditions, and privileging local/(i)Indigenous knowledge production and ways of doing and being. This critical understanding of context unsettles dominant notions of context by focusing on power relationships. I hope that my story will add to the growing discussion regarding alternative modes of practice and education that counter dominant Westernized individualized social work perspectives and promote decolonized approaches.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".