Nineteenth International Conference on Grey Literature, “Public Awareness and Access to Grey Literature”
Bibliographic record
Abstract
Two of the most formidable problems that have faced information through the years are its overload on the one hand and its loss on the other. These are seen as interconnected with the supply and demand sides of grey literature. A quarter century ago, the Grey Literature Network Service joined by research communities in library and information, physics, karst and marine sciences, bio-medicine, nuclear energy, archeology, and many other scientific and technical fields set out to address this loss and overload of information. In 1992, when the call for papers went out for the first conference in the GL-Series, the response was predominantly focused on the demand side of grey literature – that which was difficult to find and even more to access. The emphasis then lie in stemming the loss of grey literature. However, the outcome of that first conference also called attention to the equally important need for further research into the supply side of grey literature – namely its production, publication, and public awareness. GL19 seeks to demonstrate how researchers and authors in the last 25 years have made significant inroads in responding to the loss and overload of grey literature. Likewise, this conference seeks to provide new directions in achieving public awareness and access to grey literature on an ever changing information landscape.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".