How Many Is Too Many? On the Relationship between Research Productivity and Impact
Bibliographic record
Abstract
Over the last few decades, the institutionalisation of quantitative research evaluations has created incentives for scholars to publish as many papers as possible. This paper assesses the effects of such incentives on individual researchers' scientific impact, by analysing the relationship between their number of articles and their proportion of highly cited papers. In other words, does the share of an author's top 1% most cited papers increase, remain stable, or decrease as his/her total number of papers increase? Using a large dataset of disambiguated researchers (N = 28,078,476) over the 1980-2013 period, this paper shows that, on average, the higher the number of papers a researcher publishes, the higher the proportion of these papers are amongst the most cited. This relationship is stronger for older cohorts of researchers, while decreasing returns to scale are observed for recent cohorts. On the whole, these results suggest that for established researchers, the strategy of publishing as many papers as possible did not yield lower shares of highly cited publications, but such a pattern is not always observed for younger scholars.
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How this classification was reachedexpand
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 | 0.041 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.077 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".