Latent Class Analysis and the Need for Clear Reporting of Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Latent Class Analysis and the Need for Clear Reporting of Methods Emily MacLean, Emily MacLean Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, CanadaMcGill International TB Centre, McGill University, Montreal, Canada Correspondence: E. MacLean, McGill University, Montreal, Quebec, Canada (emily.maclean@mail.mcgill.ca). https://orcid.org/0000-0001-9272-0985 Search for other works by this author on: Oxford Academic PubMed Google Scholar Nandini Dendukuri Nandini Dendukuri McGill International TB Centre, McGill University, Montreal, CanadaDepartment of Medicine, McGill University, Montreal, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Clinical Infectious Diseases, Volume 73, Issue 7, 1 October 2021, Pages e2285–e2286, https://doi.org/10.1093/cid/ciaa1131 Published: 06 August 2020 Article history Received: 20 July 2020 Editorial decision: 26 July 2020 Accepted: 31 July 2020 Published: 06 August 2020 Corrected and typeset: 02 December 2020
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it