Access and Document Supply: a comparative study of grey literature
Bibliographic record
Abstract
The report addresses the different aspects of the accessibility and dissemination of grey literature in the digital age where the de-materialization of documents has led to a new paradigm that has superseded the intrinsic characteristics of printed material. Based on the added value of grey literature for academic institutions, the report attempts to provide an analysis of the ongoing transformations, especially concerning the way in which research and development in the area of grey literature have become part of the open access movement. In this context, we will analyse some of the major public supply services for the dissemination of grey literature: their typology, their strategic approach, and the special conditions and characteristics of their service. What are their projects with regard to grey literature and the open access movement? What is the impact of these projects on document supply, acquisition policy and the information system? For the study, we selected five public institutions: the British Library (UK), the CISTI (Canada), INIST (France), KISTI (Korea) and the TIB Hannover (Germany). We excluded networks and corporate profitbased suppliers.
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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.059 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".