Bibliographic record
Abstract
Bibliometrics is the science that addresses the forms of production, contents, dissemination and effects (mainly in terms of impact) of publications via statistical tools. The greatest interest of bibliometrics (or scientometrics, when restricted to academic publications) lies in allowing the study of large bibliographic productions with empirical tools, thus achieving systematic portraits of the evolution and state of the art of scientific disciplines in a way that individual researchers could not achieve based solely on their own readings. The main objects of study of bibliometrics are the diachronic evolution of a field of study, its current trends, thematic and methodological axes, productivity, authorship patterns—whether individual, institutional or national—and impact in terms of citations and visibility on the Internet. This entry briefly presents bibliometrics as a whole. It dwells in particular on its main objects of study, as well as on its potentialities and limitations, then focuses on its methodological tools—mainly quantitative and statistical—and concludes with a portrait of its application to translation studies until 2019.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.058 | 0.095 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.061 |
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".