Bibliographic record
Abstract
This paper concerns the relation between critical reflective practice and social workers’ lived experience of the complicated and contradictory world of practice. I will outline how critical reflection based on discourse analysis may generate useful perspectives for practitioners who struggle to make sense of the gap between critical aspirations and practice realities, and who often mediate that gap as a sense of personal failure. I will describe two examples of discourse-based case studies, and show how the conceptual space that is opened by such reflection can help social workers gain a necessary distance from the complexity of their ambivalently constructed place. Discourse analysis can provide new vantage points from which to reconstruct practice theory in ways that are more consciously oriented to our social justice commitments. I understand these vantage points in the case studies I will describe as: 1) an historical consciousness, 2) access to understanding what is left out of discourses in use, 3) understanding of how actors are positioned in discourse, all leading to: 4) a new set of questions which expose the gap between the construction of practice possibilities and social justice values, thus allowing for a new understanding of the limitations, constraints and possibilities within the context of the practice problem.
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 | 0.127 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.021 | 0.179 |
| Scholarly communication | 0.041 | 0.046 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".