Bibliographic record
Abstract
Abstract Bibliometrics has been both studied and applied in a variety of research fields, such as Library and Information Science (LIS), Sociology, history of science, business, management and research policy. In the LIS field, however, there has been an interesting phenomenon: Bibliometrics is quite strong in research as seen from bibliometric maps of LIS literature, but very weak in education as seen from LIS course offerings. This phenomenon invites serious questions, such as Why is this the case? Who is doing Bibliometrics? Where and how do they get their training? Are these kinds of training enough for conducting quality research? Why or why not should we strengthen Bibliometrics education in LIS programs in North America? What should be a proper place of Bibliometrics in LIS education? The panelists, who are both Bibliometrics researchers and university LIS educators from different regions of the world, will share their views of these and related questions. The panel will start with opening remarks from each panelist in the format of 20 × 20 presentations, and will then open the floor for discussion among the panelists and with the audience. This panel is expected to benefit both research and education in LIS.
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 | 0.105 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.043 | 0.054 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.033 | 0.057 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".