Forms and Functions of Author Keywords in Theses and Dissertations at the UNESP Institutional Repository (Brazil)
Bibliographic record
Abstract
This research aimed to prepare guidelines for authors by investigating forms and functions of keywords assigned by authors in theses and dissertations defended in 2023 in the Graduate Program in Information Science at Unesp. The exploratory and descriptive study utilized a sample collected in the Unesp Institutional Repository. A corpus of 31 theses and 14 dissertations submitted to the Unesp Institutional Repository comprised a total of 183 keywords in Portuguese without duplicates and an average of 4.7 keywords, considering 213 keywords with duplicates. The analysis results initially identified that the Repository has a tutorial on using the Unesp Thesaurus to control vocabulary and that the authors use natural language to assign keywords. The findings reveal that, out of the 183 keywords, 89 (48\%) are exclusive, singular and specific to the area of Information Science, candidates for descriptors in the Unesp Thesaurus. The other 94 keywords (51.3\%) have 40 (21.3\%) exact descriptors, and the other 54 (29.5\%) present forms and functions that serve as examples for inclusion in the tutorial instructions. Based on the results obtained, it is concluded that the percentage of 21\% overlap between keywords and descriptors reveals that the Unesp Thesaurus was consulted by the authors when filling out keyword metadata and that the low number of exact descriptors and exclusive keywords indicate that they need to be included as new terms. It is recommended, therefore, to define an Indexing Policy that considers the need for hybrid coexistence between natural language and vocabulary control.
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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".