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Record W4220783886 · doi:10.1287/orsc.2022.1593

Settlement Constellations and the Dynamics of Fields Formed Around Social and Environmental Issues

2022· article· en· W4220783886 on OpenAlexaff
Sean Buchanan, Charlene Zietsma, Dirk Matten

Bibliographic record

VenueOrganization Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsYork UniversityUniversity of Manitoba
Fundersnot available
KeywordsTypologySettlement (finance)ConstellationField (mathematics)Organizational fieldOrganizational theorySociologyOrganizational structureEconomic geographyPolitical scienceInstitutional theorySocial scienceBusinessGeographyEconomicsManagement

Abstract

fetched live from OpenAlex

Firms are increasingly responding to social and environmental issues in highly complex and heterogeneous organizational fields that transcend national boundaries. Yet, we still have a limited understanding of how these fields are structured and the implications of structural variation on how issues are addressed over time. We advance theory in this area by arguing that issue fields are characterized by varying settlement constellations that structure these fields. We develop a typology of three settlement constellations—unified, fragmented, and bifurcated—and describe their impact on field structure and the challenges they raise for addressing field-defining issues. We then theorize the evolution of fields with different settlement constellations and explain how and why constellations are sustained over time as well as when they may change. Our paper helps advance theory on organizational fields, private regulation, and firm responses to social and environmental issues. More broadly, our paper highlights the unique position of organizational and institutional scholars to examine complex social and environmental issues, or “grand challenges.”

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designObservational
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

Citations27
Published2022
Admission routes1
Has abstractyes

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