OpenSIGLE - Crossroads for Libraries, Research and \nEducational Institutions in the field of Grey Literature
Bibliographic record
Abstract
This poster is based on a paper presented at \nthe Tenth International Conference on Grey Literature (GL10) in which GreyNet's collections of \nconference preprints were made accessible via the OpenSIGLE Repository. OpenSIGLE offers a unique \ndistribution channel for European grey literature with roots dating back a quarter century. The \nexperience of INIST as service provider and GreyNet as data provider will be further discussed \nincluding recent developments. The poster closes with a draft proposal that seeks to explore the \ncapacity required for the OpenSIGLE Repository to develop in multilateral and international \ncooperation in support of European research infrastructures committed to the open access of grey \nliterature collections and resources. Emphasis is placed on the involvement of libraries, research \ncenters, and institutions of higher education, as well as, requirements for a grey literature \nnetwork service to sustain further development, exploitation, and promotion of the OpenSIGLE \nRepository.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.168 | 0.071 |
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".