Speaking of that: Terms to avoid or reconsider in the eating disorders field
Bibliographic record
Abstract
Inspired by an article on 50 terms that, in the interest of clarity in scientific reasoning and communication in psychology, psychiatry, and allied fields, "should be avoided or at most be used sparingly and only with explicit caveats,"(1) we propose a list of terms to avoid or think twice about before using when writing for the International Journal of Eating Disorders (IJED). Drawing upon our experience as reviewers or editors for the IJED, we generated an abridged list of such terms. For each term, we explain why it made our list and what alternatives we recommend. We hope that our list will contribute to improved clarity in scientific thinking about eating disorders, and that it will stimulate discussion of terms that may need to be reconsidered in our field's vocabulary to ensure the use of language that is respectful and sensitive to individuals who experience an eating disorder.
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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 | 0.046 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".