Autoarchivo de artículos biomédicos en repositorios de acceso abierto
Bibliographic record
Abstract
INTRODUCTION AND DEVELOPMENT: Open-access literature is digital, online, free of charge, and free of most copyright and licensing restrictions. Self-archiving or deposit of scholarly outputs in institutional repositories (open-access green route) is increasingly present in the activities of the scientific community. Besides the benefits of open access for visibility and dissemination of science, it is increasingly more often required by funding agencies to deposit papers and any other type of documents in repositories. In the biomedical environment this is even more relevant by the impact scientific literature can have on public health. However, to make self-archiving feasible, authors should be aware of its meaning and the terms in which they are allowed to archive their works. In that sense, there are some tools like Sherpa/RoMEO or DULCINEA (both directories of copyright licences of scientific journals at different levels) to find out what rights are retained by authors when they publish a paper and if they allow to implement self-archiving. PubMed Central and its British and Canadian counterparts are the main thematic repositories for biomedical fields. In our country there is none of similar nature, but most of the universities and CSIC, have already created their own institutional repositories. CONCLUSION: The increase in visibility of research results and their impact on a greater and earlier citation is one of the most frequently advance of open access, but removal of economic barriers to access to information is also a benefit to break borders between groups.
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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.058 | 0.052 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.123 |
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".