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Entrepreneurship and the construction of value in biotechnology

2010· book-chapter· en· W1491701704 on OpenAlexaff
Sarah Kaplan, Fiona Murray

Bibliographic record

VenueResearch in the sociology of organizations · 2010
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsValue (mathematics)DutyConventionEntrepreneurshipAction (physics)Process (computing)Interpretation (philosophy)CreativityPolitical scienceNeoclassical economicsEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.282
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations89
Published2010
Admission routes1
Has abstractyes

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