How efficient are German life sciences? Econometric evidence from a latent class stochastic output distance model
Bibliographic record
Abstract
This article investigates the technical efficiency in German higher education while accounting for possible heterogeneity in the production technology. We investigate whether a latent class model would identify the different sub-disciplines of life sciences in a sample of biology and agricultural units based on technological differences. We fit a latent class stochastic frontier model to estimate the parameters of an output distance function formulation of the production technology to investigate if a technological separation is meaningful along sub-disciplinary lines. We apply bootstrapping techniques for model validation. Our analysis relies on evaluating a unique dataset that matches information on higher educational institutions provided by the Federal Statistical Office of Germany with the bibliometric information extracted from the ISI Web of Science Database. The estimates indicate that neglecting to account for the possible existence of latent classes leads to a biased perception of efficiency. A classification into a research-focused and teaching-focused decision-making unit improves model fit compared to the pooled stochastic frontier model. Additionally, research-focused units have a higher median technical efficiency than teaching-focused units. As the research focus is more prevalent in the biology subsample an analysis not considering the potential existence of latent classes might misleadingly give the appearance of a higher mean efficiency of biology. In fact, we find no evidence of a difference in the mean technical efficiencies for German agricultural sciences and biology using the latent class model.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".