Not Quite this and not Quite that: Anorexia Nervosa, Counselling Psychology, and Hermeneutic Inquiry in a Tapestry of Ambiguity
Bibliographic record
Abstract
As a group of researchers exploring how to best understand the complex topic of families discovering their loved one has anorexia nervosa (AN), we found that we had to weave ambiguity into our design. Embracing ambiguity allowed us to create a tapestry that acknowledges the ambiguity of AN, counselling psychology (and other helping professions), and hermeneutic inquiry. In fact, the “not quite this and not quite that” features of these three constructs emerged as the thread that holds the inquiry together. We review the topic of AN through a lens of ambiguity. Further, we position both the field of counselling psychology and the research method of hermeneutic inquiry as compatible frameworks in the study of AN, in both practice and research. By acknowledging, and at times even embracing, ambiguity, we respect the complexity of the situation we are studying.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.097 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".