MétaCan
Menu
Back to cohort
Record W3092563420 · doi:10.3390/su12208402

Perceiving Agency in Sustainability Transitions: A Case Study of a Police-Hospital Collaboration

2020· article· en· W3092563420 on OpenAlexaff
Michael Halinski, Linda Duxbury

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsCarleton UniversityToronto Metropolitan University
Fundersnot available
KeywordsAgency (philosophy)SustainabilityLegitimacyContext (archaeology)PerceptionPublic relationsBusinessQualitative researchPolitical sciencePsychologySociologyPoliticsGeography

Abstract

fetched live from OpenAlex

This paper explores how agency was used within a police-hospital collaboration to implement a planned change designed to increase the sustainability of a cross-sector collaboration. A longitudinal, qualitative case study involving pre-and-post interviews with 20 police officers and 20 healthcare workers allowed us to capture multiple perspectives of the planned change over time. Analysis of case study data reveals three major findings: (1) organizations with limited power can have agency in cross-sector collaborations when they are perceived to have legitimacy and urgency; (2) the extent to which the implementation of a planned change influences perceptions of agency depends on the organizational context of the perceiver; and (3) different levels of analysis (i.e., meso versus micro) support different conclusions with respect to the role of agency in the sustainability transition process. More broadly, our study highlights the role of perception when investigating agency within sustainability transitions.

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)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.015
Scholarly communication0.0070.007
Open science0.0020.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.253
Teacher spread0.243 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicManagement and Organizational StudiesFrench-language works237,207