The decade of metrics? Examining the evolution of metrics within and outside LIS
Bibliographic record
Abstract
Abstract Editor's Summary Since its start in the mid‐19th century, the measurement of document attributes and interrelationships has evolved into the formal field of bibliometrics, becoming solidly established in information science and reaching into other domains. Bibliometric methods have been used to shed light on organizational structure of library and information science (LIS), the reach and influence of LIS research and on interdisciplinarity and the emergence of new fields of study. Data from citation indexes from 1900 through 2011 show the rise of metrics, with the decade starting in 2010 likely to be the most productive and influential. The most cited researchers in information science in 2010 and 2011 focus on metrics, and the use of the term bibliometri* has surged since 2000. The study of metrics in medicine is increasing rapidly, though less so in the social sciences, humanities and natural sciences. Among LIS subtopics, metrics appears to have the strongest influence outside the field.
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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 | 0.032 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".