Bibliographic record
Abstract
In the beginning (of bibliometrics), citation counts of academic research were generated to be used in annual calculations to express a research journal’s impact. Now those same citation counts make up a social graph of scholarly communication that is used to measure the research strengths of authors, the hotness of their papers, the topic prominence of their disciplines, and assess the strength of the institutions where they are employed. More troubling, the publishers of this emerging social graph are in the process of enclosing scholarship by trying to exclude the infrastructure of libraries and other independent, non-profit organizations invested in research. This paper will outline efforts currently being employed by scholarly communication librarians using platforms built by organizations such as Our Research’s UnPaywall and Wikimedia’s Wikidata Project so that the commons of scholarship can remain open. Strategies will be shared so that researchers can adapt their workflows so that they might allow their work to be copied, shared, and be found by readers widely across the commons. Scholars will be asked to make good choices.
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 | 0.018 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".