MétaCan
Menu
Back to cohort

Evolutionary Dynamics of Organizational Populations and Communities

2021· book-chapter· en· W3170977138 on OpenAlexaff
Joel A. C. Baum, Hayagreva Rao

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrganizational fieldOrganizational ecologyEconomic geographyDeregulationPopulationOrganizational structureGlobalizationTechnological changePhenomenonEconomic systemOrganizational changeField (mathematics)Organizational studiesBusinessOrganization developmentPolitical sciencePublic relationsSociologyEconomicsMarket economyInstitutional theoryManagement

Abstract

fetched live from OpenAlex

Abstract Evolution is conceptualized as a multi-level phenomenon (subunit–organization–organizational field–national economy) that links organizational and ecological systems. Analysis of population and community-level evolution emphasizes the roles of institutional change (e.g., industry deregulation, globalization, market reforms), technological innovation cycles (e.g., technological discontinuities, dominant designs), entrepreneurs, and social movements as triggers of organizational variation. Institutional and technological change transforms the dynamics of organizational communities by shifting the boundaries of organizational forms, destabilizing or reinforcing existing community structures, giving rise to consensus and/or conflict oriented social movements, and creating opportunities for entrepreneurs and venture capitalists to shape new organizational forms.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.194
Teacher spread0.163 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicInnovation and Knowledge ManagementFrench-language works237,207