Bibliographic record
Abstract
Social workers have always used narratives in the service of their clients. Many of us spend half our days listening to stories and the other half repeating them in one form or another, whether in assessments, in advocating for services or for a more accurate understanding of a client's circumstances. While we excel at this kind of storytelling, we have been held back from using the narrative genre in telling our own story. That story is one that describes the intricacies and variety of social work practice as well as the uniqueness that distinguishes us from other helping professions. For hospital social workers, who have experienced profound change in recent years, it is especially important that we find innovative and interesting ways to convey a richer and deeper understanding and appreciation of our role. The genre of personal narrative allows us to do this in a voice suitable for the task. When narratives are used in this way they can be seen as a tool of advocacy for both ourselves and our clients (Chambon, 2004).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".