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Record W4401684095 · doi:10.1111/joms.13134

The Political Side of Social Enterprises: A Phenomenon‐Based Study of Sociocultural and Policy Advocacy

2024· article· en· W4401684095 on OpenAlexfundno aff
Johanna Mair, Nikolas Rathert

Bibliographic record

VenueJournal of Management Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersUniversità BocconiUniversity of OxfordImperial College LondonMcGill University
KeywordsPhenomenonSociocultural evolutionPoliticsPolitical sciencePolitical economySociologyLawEpistemology

Abstract

fetched live from OpenAlex

Abstract This study explores the often‐overlooked political dimension of social enterprises, particularly their advocacy activities aimed at influencing public policy, legislation, norms, attitudes, and behaviour. While traditional management research has focused on commercial activity and the beneficiary‐oriented aspects of social enterprises, this paper considers their upstream political activity. Using a phenomenon‐based approach, we analyse original survey data from 718 social enterprises across seven countries and six problem domains to identify factors associated with their engagement in advocacy. Our findings reveal that public spending and competition in social enterprises’ problem domains, as well as their governance choices – legal form, sources of income, and collaborations – are significantly associated with advocacy activities. We propose a new theoretical framework to understand these dynamics, positioning social enterprises as key players in markets for public purpose. This research underscores the importance of recognizing the political activities of social enterprises and offers new insights for studying hybrid organizing and organizations that address complex societal challenges. By highlighting the integral role of advocacy, our study contributes to a more comprehensive understanding of how social enterprises drive social change, not only through direct service provision but also by shaping the broader sociopolitical environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.421
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.390
Teacher spread0.351 · 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.

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

Citations16
Published2024
Admission routes1
Has abstractyes

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