The value of science: changing conceptions of scientific productivity, 1869 to circa 1970
Bibliographic record
Abstract
Productivity is now a buzzword in science studies. Whether you consult the literature on research management, the economic literature on technology and innovation, the literature on bibliometrics or the official literature on science policy and its conceptual frameworks, what you find is analyses on productivity, often accompanied by a plea, and recipes, for increased productivity. This article documents how the concept of productivity got into the analysis of science, through the statistics on which the concept rested, and its transformation over one hundred years. It argues that, through history, the concept as applied to science has carried four meanings: productivity as reproduction, productivity as output, productivity as efficiency and productivity as outcome.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".