Bibliographic record
Abstract
Purpose – The inaugural installment of the column data deluge and open knowledge comes at the close of a year which saw changes, developments and new beginnings for libraries in the areas of linked data, open data, metadata, open access publishing and other related movements. Design/methodology/approach – The methodology adopted is a literature review. Findings – Sometimes, changes in the information environment present themselves like towering waves crashing into rugged cliffs and librarians stand at the edge in awe of the spectacle. At other times, despite the crashing waves, librarians lead massive projects to build the standards and infrastructure to capture the water and direct its flow. Practical implications – The overall trend for the latter librarians is toward developing and adopting new ideas, methods, approaches and services to support finding and sharing data in an increasingly large and complex online context. As many of author’s colleagues have commented in recent years, “now is an exciting time to be a librarian”. Originality/value – The author heartily agree and look forward to sharing, through this column, the highlights of these exciting times.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".