Bibliographic record
Abstract
International Conference on Developments in education December 5, 2022 14-00 Canada, Toronto DOI: https://doi.org/10.5281/zenodo.7514469 The conference will start at 14:00 on the Zoom platform. Moderator: Subash Gupta Speakers Tea Mchedluri –Education in new life. Iakob Gogebashvili Telavi State University, Georgia. Kartuli Universitety Street 1, Telavi, Doctor of Biological Sciences. Professor. E-mail: t.mchedluri@yahoo.com Tsisana Kolotadze- Biological sciences in real life. Iakob Gogebashvili Telavi State University, Georgia. Kartuli Universitety Street 1, Telavi, E-mail: Cisana.kolotadze@tesau.edu.ge Lika.Mosemgvdlishvili - Practice of eastern thinkers Iakob Gogebashvili Telavi State University, Georgia. Kartuli Universitety Street 1, Telavi, E-mail: Lika.mosemgvdlishvili@tesau.edu.ge Tamar Noniashvili- Students linguistic competence Iakob Gogebashvili Telavi State University, Georgia. Kartuli Universitety Street 1, Telavi Agzamova Shoira Abdusalamovna- Role of vitamin D Professor of the Department of Family Medicine No. 1, Physical Education, Civil Defense of the Tashkent Pediatric Medical Institute, Doctor of Medical Sciences Saodat Rustamova - The management system of JSC “ailways of Uzbekistan Academy of Public Administration under the President of the Republic of Uzbekistan
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.389 | 0.172 |
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".