Bibliometrics: Methods for studying academic publishing
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
- Consensus categories
- Metaresearch, Bibliometrics
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: none
- Teacher disagreement score
- 0.917
- Threshold uncertainty score
- 0.999
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.768 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.137 | 0.417 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.044 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Bibliometrics is the study of academic publishing that uses statistics to describe publishing trends and to highlight relationships between published works. Likened to epidemiology, researchers seek to answer questions about a field based on data about publications (e.g., authors, topics, funding) in the same way that an epidemiologist queries patient data to understand the health of a population. In this Eye Opener, the authors introduce bibliometrics and define its key terminology and concepts, including relational and evaluative bibliometrics. Readers are introduced to common bibliometric methods and their related strengths and weaknesses. The authors provide examples of bibliometrics applied in health professions education and propose potential future research directions. Health professions educators are consumers of bibliometric reports and can adopt its methodologies for future studies.
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.
The record
- Venue
- Perspectives on Medical Education
- Topic
- scientometrics and bibliometrics research
- Field
- Decision Sciences
- Canadian institutions
- Royal College of Physicians and Surgeons of CanadaUniversity of Ottawa
- Funders
- not available
- Keywords
- BibliometricsTerminologyPublishingData scienceStrengths and weaknessesPopulation healthComputer sciencePopulationLibrary scienceMedicinePsychologyPolitical science
- Has abstract in OpenAlex
- yes