Using the Web of Science to Populate Faculty Articles in an Institutional Repository
Bibliographic record
Abstract
Background: Faculty at Central Washington University (CWU) were not depositing and preserving their research articles in the University Institutional Repository (IR), so an alternative method to identify and include faculty scholarship in the IR was developed. Librarians used the Web of Science to discover articles published by the CWU faculty and then deposited them in the IR. Analysis: Thousands of articles written by CWU faculty were located and deposited. This project increased interaction with the IR from outside the library and the university beyond any expectations. Conclusion and implications: This was a successful project, but it required a useful interface to locate the metadata and librarians with highly technical skills.RésuméContexte: Les facultés à Central Washington University (CWU) ne déposaient ni ne preservaient leurs articles de recherche au dépôt institutionnel, alors une méthode alternative d’identifier et inclure la bourse de la faculté dans ce dernier a été développé. Les documentalistes utilisaient le Web of Science pour découvrir les articles publiés pa la faculté de CWU et ensuite les déposer au dépôt institutionnel. Analyse: Des milliers d’articles écrits par la faculté de CWU on été retrouvé et déposé. Ce projet a augmenté considérablement les interactions externes avec le dépôt institutionnel. Conclusion et implications: Ce projet fut un succès mais nécessiterait d’utiliser une interface permettant de localiser les metadata et les documentalistes grâce à de grandes compétences techniques.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".