Collecting Bibliographic References: A Bibliometric Analysis of CiteULike's Collection as Grounds for In-Depth Interviews
Bibliographic record
Abstract
CiteULike helps users manage and share bibliographic references on the Web. This study is based on a bibliometric analysis of these references. The findings will lead to email-based interviews with researchers recruited from CiteULike. The goal is to further our knowledge of the needs and practices of researchers and to explore implications for academic libraries.CiteULike aide les usagers à gérer et à partager des références bibliographiques sur le Web. Cette étude se base sur une analyse bibliométrique de ces références. Les résultats mèneront à des entrevues par courrier électronique avec des chercheurs, recrutés au moyen de CiteULike. L’objectif est d’accroître notre connaissance des besoins et des pratiques des chercheurs et d’en explorer l’implication pour les bibliothèques universitaires.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.099 | 0.340 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".