Bibliographic record
Abstract
This document reflects the distributed administrative structure to be put into practice by the Implementing New Knowledge Environments (INKE) group for the purpose of governing itself as it carries out work on its Major Collaborative Research Initiative (MCRI)-funded initiative. The INKE group consists of academic researchers, academic research partners (many invested as stakeholders as well), an international advisory board, a partners committee, individual research area groups (RAG) each with their own (co)leads who act as administrators for the group and form the overall RAG administrative group committee, and an executive committee (EC) that represents all areas of activity in the research endeavour and also includes an administrative/ management advisor (who carries out work and provides leadership on process, not research content) and a project manager. Taken as a whole, the structure of the group is an embodiment of the distributed administrative and authoritative principles that have evolved over the several years of the project's foundation, and the materials that follow have been assembled and authored by the entirety of the administrative team in that spirit. This document is also closely aligned with the processes outlined in two related documents: the annual calendar and the annual RAG planning process.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.202 | 0.279 |
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".