SEEKING SOLUTIONS WITHOUT CENTERING PROBLEMS: FROM RESEARCH TO PRACTICE
Bibliographic record
Abstract
While it is often a commitment to social justice ideals that bring people to the helping professions as practitioners, our theories and approaches to care are often service-oriented and expert-driven. Such an orientation to helping often focuses more on individual change – that is, changing those who are experiencing difficulties – than it does on systemic or collective change. The current article offers one very accessible possibility for practitioners who have adolescent clients. By incorporating photo elicitation into the helping relationship, the social nature of “social” problems can be acknowledged and attended to, gently nudging the boundaries of practice in a way that is more contextualized, centering possibilities rather than problems.
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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 | 0.229 | 0.242 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.035 | 0.048 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".