A Concise Review On The Role Of Author Self-Citations In Information Science, Bibliometrics And Science Policy
Bibliographic record
Abstract
The objective of the present study is twofold: (1) to show the aims and means of quantitative interpretation of bibliographic features in bibliometrics and their re-interpretation in research policy, and (2) to summarise the state-of-art in self-citation research. The authors describe three approaches to the role of author self-citations and possible conflicts arising from the different perspectives.L’objectif de cette étude est de caractériser les effets de quantification comme appliqués dans la bibliométrie et leur interprétation en politique de recherche. Nous présentons un sommaire de la recherche en ce qui concerne le phénomène des auto-citations et nous décrivons trois possibilités d’expliquer les auto-citations aussi bien que les conflits qui résultent de ces points de vues.
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
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.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.046 | 0.634 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.160 | 0.537 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".