Individual researchers’ research productivity: a comparative analysis of counting methods
Bibliographic record
Abstract
Introduction Productivity can be studied at different scales (e.g., country, organisation, author). The present work examines productivity at the researcher level, with the financial support received by researchers representing input and researchers’ papers representing output. Regardless of the scale at which productivity is examined, science must be considered a collective endeavour, particularly since there is a growing trend towards more collaboration in nearly every field. Importantly though, very distinct collaboration practices exist across fields of research. For instance, over 90% of the papers in the natural sciences and engineering (NSE) are written in collaboration (more than one author), whereas this proportion is 60% in the social sciences and 10% in the humanities (Lariviere, Gingras and Archambault, 2006). Whether one uses fractional or whole counts can be expected to yield hugely different productivity measures (Lindsey, 1980; Egghe, Rousseau, and Van Hooydonk, 2000; Gauffriau, M. et al., 2008). This paper examines how fractional versus whole-paper counting affects the measurement of researchers’ performance in the social sciences and the humanities (SSH) versus in the NSE.
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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 | 0.290 | 0.635 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.037 | 0.050 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".