Bibliographic record
Abstract
This deliverable presents the latest version of the draft of E-RIHS ERIC Statutes (E- RIHS_Statutes_V5.1_2020.03.23) at the time of its writing. It describes the work process that lead to the present document, and it discusses the possible future changes in the lifetime of the E-RIHS ERIC Statutes. The first phase of the work process was based on an exhaustive comparative study of the active ERICs. The first version was discussed within the preparatory phase. The second phase was the discussion and the improvement within the E-RIHS Stakeholders Advisory Board, a board of national ministerial representatives not limited to members of E-RIHS PP. The third phase, the current one, is defined by the involvement of the E-RIHS Interim General Assembly. This new body acts in a decision-making capacity in order to prepare the step 1 submission of the necessary documents to become an ERIC. The next phases will be the final and official exchanges with the European Commission after the step 1 submission, and then the operational phase of E-RIHS ERIC. DISCLAIMER: This document reflects the state of advancement of the preparatory work at the time of its delivery. As such, its content may be subject to further evolution. It does not reflect official commitments or positions of the E-RIHS Interim General Assembly members, nor does it preclude other members to join the E-RIHS Interim General Assembly.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.274 | 0.232 |
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".