Innovation in eating disorders research and practice: Expanding our community and perspectives at the 2018 International Conference on Eating Disorders: Editorial to accompany IJED Virtual Issue in honor of the 2018 International Conference on Eating Disorders.
Bibliographic record
Abstract
OBJECTIVE: This virtual issue of the International Journal of Eating Disorders (IJED) highlights the excellent and innovative research and practice discussed at the 2018 International Conference on Eating Disorders held in Chicago, Illinois, USA. METHOD AND RESULTS: The virtual issue contains a series of articles recently published in IJED, which we have curated to reflect and expand on the insights delivered during the conference keynote and plenary presentations. DISCUSSION: In line with the conference theme of Innovation in Research and Practice: Expanding our Community and Perspectives, we hope this collection of articles will spark new ideas for research, practice, and collaboration to accelerate knowledge on eating disorder risk factors and recovery, and the reach and impact of evidence-based treatment, prevention, and policy efforts.
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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".