Thinking Critically About Appraising FOAM
Bibliographic record
Abstract
[para. 1]: " The dawn of the 21st century has certainly left us with new issues and problems that we have seldom encountered before. “Fake news,” misinformation are rife on social media, and the need for increased criticality in reading anything in our daily lives has never been so important. Critical appraisal is now being taught by elementary school teachers and encouraged all along the educational spectrum; online medical education should not be exempt from these discussions. As such, we thank Drs. Munz and Chen for their commentary on our paper which highlights many key findings in our study and agrees with us that it is imperative for our discipline to bring critical appraisal to online educational resources."
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 | 0.158 | 0.439 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".