EIFL and Library Group Comments on Updated Draft WIPO CMO Toolkit (2021)
Bibliographic record
Abstract
EIFL and partner organizations in the library, archives and museum communities responded to a public consultation to provide additional comments on the updated draft WIPO Good Practice Toolkit for Collective Management Organizations (CMOs), released on 27 May 2021. Publication of the updated draft Toolkit follows an earlier consultation that took place in April 2021.\nThe updated version of the Toolkit contains an expanded section on supervision and monitoring of CMOs (Section 13). We noted three concerns in the updated Section 13, in particular. In our comments, we propose a number of amendments to address the concerns in Section 13, along with suggestions in other parts of the text to provide clarifications or to iron out ambiguities.\nThe final, updated version of the CMO Toolkit will be published at the end of September 2021.\nThe additional were submitted to WIPO together with the Canadian Federation of Library Associations (CFLA), the International Council of Archives (ICA), the International Council of Museums (ICOM), the International Federation of Library Associations and Institutions (IFLA), and the Society of American Archivists (SAA).
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.
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 | 0.062 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.145 | 0.097 |
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".