The emergence and growth of an improbable laboratory in economics and management: the case of BETA
Bibliographic record
Abstract
Abstract The aim of this contribution is to trace the process by which such an improbable research unit, the BETA at the University Louis Pasteur, has emerged and grown and to stress the interesting outcomes. The paper can be read as a case study on the emergence and development of a ‘knowledge creating community’. Such an analysis requires the understanding of the micro‐dynamics of the complex co‐evolution between four different elements in the production of research: small epistemic communities as active units of production of specialised knowledge in different domains of economics and management, the lab as a research institution, the University as the locus of collective representation, and key individuals as boundary spanners. In particular, the analysis of this evolution reveals the importance of the earliest stages of the collective effort that necessitates the interaction and coordination of dispersed actors. From these interactions progressively emerged a coherent unit of production of knowledge with shared codes, a common ‘code‐book’, and shared experiences. The real recognition of the lab as an institution came much later (recognition by the French CNRS ‐ 1985). From this moment, the lab as an institution had to show its capability to sustain both cumulative progress and some turnover of personnel.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".