Bibliographic record
Abstract
This paper examines the tagging practices evident on CiteULike, a research oriented social bookmarking site for journal articles. Tagging practices were examined using standard informetric measures for analysis of bibliographic information and term use. Additionally, tags were compared to author keywords and descriptors assigned to the same article.Cette communication examine les pratiques d’étiquetage par mots-clés qui sont utilisés sur CiteULike, un service d’étiquetage social, pour les articles de périodiques. Ces pratiques de marquage ont été examinées en utilisant les mesures informétriques habituellement utilisées pour l’analyse d’information bibliographique et d’utilisation de mots-clés. En outre, les étiquettes ont été comparées aux mots-clés utilisés par les auteurs et aux descripteurs attribués à ces mêmes articles.
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 | 0.036 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".