Within and across department variability in individual productivity : the case of economics
Why this work is in the frame
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Bibliographic record
Abstract
University departments (or research institutes) are the governance units in any scientific field \nwhere the demand for and the supply of researchers interact. As a first step towards a formal \nmodel of this process, this paper investigates the characteristics of productivity distributions \nof a population of 2,530 individuals with at least one publication who were working in 81 \nworld top Economics departments in 2007. Individual productivity is measured in two ways: \nas the number of publications until 2007, and as a quality index that weights differently the \narticles published in four journal equivalent classes. The academic age of individuals, \nmeasured as the number of years since obtaining the PhD until 2007, is used to measure \nproductivity per year. Independently of the two productivity measures, and both before and \nafter age normalization, the main findings of the paper are the following five. Firstly, \nindividuals within each department have very different productivities. Secondly, there is not \na single pattern of productivity inequality and skewness at the department level. On the \ncontrary, productivity distributions are very different across departments. Thirdly, the effect \non overall productivity inequality of differences in productivity distributions across \ndepartments is greater than the analogous effect in other contexts. Fourthly, to a large \nextent, this effect on overall productivity inequality is accounted for by scale factors well \ncaptured by departments’ mean productivities. Fifthly, this high degree of departmental \nheterogeneity is found to be compatible with greater homogeneity across the members of a \npartition of the sample into seven countries and a residual category.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it