Entrepreneurship and the construction of value in biotechnology
Bibliographic record
Abstract
By taking conventionalist view of the evolution of biotechnology, we suggest that the process by which entrepreneurs determined what made biotechnology valuable and figured out how to organize around such an economic logic was contested. The shape that biotechnology has ultimately taken emerged from the resolution of these contests. Convention theory – as elaborated in Boltanski and Thévenot's (2006) On Justification 1 – argues that our economy is shaped by participants affecting the rules of economic action. Whereas most economists would argue that the assignment of value underpins any system of exchange, conventionalists suggest that this value is not only given by the principles of optimization but instead can be derived from many possible spheres such as civic duty, attainment of fame, proof of technologic performance, and demonstration of creativity. More specifically, Boltanski and Thévenot (2006, p. 43) claim that the establishment of a particular logic “comes about as a part of a coordinated process that relies on two supports: a common identification of market goods, whose exchange defines the course of action, and a common evaluation of these objects in terms of prices that make it possible to adjust various actions.” Simply put, economic logics embody principles of economic coordination or conventions that guide interpretation of the technology and its value.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".