Twenty-Fifth International Conference on Grey Literature "Confronting Climate Change with Trusted Grey Resources"
Bibliographic record
Abstract
With over a quarter century of research on grey literature carried out by diverse communities of practice in this field of information, a collective challenge emerges. Researchers and authors in sectors of government, non-government, academics, and business spanning manifold disciplines in science, technology, and the humanities are called to action. Their years of work dealing with the production, processing, digital publication, open access, and preservation of research outputs in multiple formats is called upon in confronting climate change. At this point in time, with the advancements in information technology available to grey literature and in accordance with FAIR data principles, researchers, authors, librarians, and other information professionals and practitioners are tasked to ensure that research outputs are findable, accessible, interoperable, and render potential reuse in furthering research and education in their respective disciplines and sectors of information. GL25 sought to accept this challenge. To this end, grey literature communities worldwide directed their attention in responding to climate change for the benefit of our vulnerable planet.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.020 |
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".