MétaCan
Menu
Back to cohort

Pragmatist Foundations of Research on Entrepreneurial Strategy

2024· article· en· W4400444425 on OpenAlexaff
Anastasia Sergeeva, David K. Reetz, Dimo Dimov, Alfonso Gambardella, Joseph T. Mahoney, Saku Mantere, Viólina Rindova, Todd Zenger

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegional Economic Development and Innovation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPragmatismEpistemologySociologyEngineering ethicsPolitical scienceManagement sciencePhilosophyEconomicsEngineering

Abstract

fetched live from OpenAlex

American pragmatism provides a rich intellectual foundation that accommodates diverse practices in service of “usefulness” and as such is well-suited to examine formation of entrepreneurial strategies that aim at changing the status quo, while facing fundamental uncertainty associated with such attempts. Research on entrepreneurial strategy drawing on pragmatism is burgeoning but remains dispersed across research communities. For example, one branch of such research focuses on how entrepreneurs adapt and evolve their ideas using scientific reasoning to test beliefs about (future) states of the world, while another branch focuses on the generative power of entrepreneurial agency employing abductive reasoning for creative world-making. There is, however, substantial potential for cross-pollination between such branches. This symposium will bring together a diverse group of leading scholars from these areas of research, to create a common ground of key pragmatist assumptions, raise open questions for future studies examining the phenomenon of entrepreneurial strategy formation, and chart opportunities for a fruitful (joint) research program.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0040.054
Scholarly communication0.0140.015
Open science0.0020.005
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.099
GPT teacher head0.355
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicRegional Economic Development and InnovationFrench-language works237,207