The Case for a new governance for IOCINDIO: a proposal for changing the status of IOCINDIO in a Sub-Commission of IOC for the Indian Ocean.
Bibliographic record
Abstract
This document was initially prepared in April 2020 by Dr M. A. Atmanand, Chairman of the IOC Regional Committee of the Central Indian Ocean (IOCINDIO), pursuant to IOC Assembly Decision IOC-XXX/3.3.4 (Paris, 26 June–4 July 2019) to consider the transformation of IOCINDIO, an IOC Regional Committee, into an IOC Sub-commission. The document benefitted from inputs from IOCINDIO Vice-chairpersons Faiza Al-Yamani, Kuwait, Mohammad Muslem Uddin, Bangladesh, and Satish S. C. Shenoi, IOC Vice-Chairperson, Electoral Group IV. The document was originally intended as a working document for the consideration of the Executive Council in 2020 before its postponement as a virtual session with a limited agenda in February 2021. Upon further reflection of the authors with the IOC Chair and senior staff of the Secretariat, a broad and inclusive consultation on this subject among IOC Member States was initiated through a virtual meeting (see IOC Circular Letter 2824) and a discussion at the next IOCINDIO session during the first quarter 2021. The progress on this issue will be reported by the Executive Secretary in his report to the Executive Council -53 and through a working document for the consideration of the Assembly in June 2021.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".