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Record W7027559600

Competitive Neutrality and the Challenge of Social Enterprise

2018· article· en· W7027559600 on OpenAlexaboutno aff

Bibliographic record

VenueUNSWorks (University of New South Wales, Sydney, Australia) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsNeutralitySalience (neuroscience)LegislationCompetition (biology)PoliticsBaseline (sea)Conceptual frameworkCompetitive advantagePublic policy
DOInot available

Abstract

fetched live from OpenAlex

Globally, the past decade has seen many countries, including the United Kingdom, United States and Canada, introducing legislation that creates distinctive hybrid legal models for social enterprise. Although Australia has no dedicated legal model for social enterprise, its growing salience poses a challenge to competitive neutrality policy regimes. The challenge posed by social enterprise is both conceptual and practical. At the conceptual level, we argue that the underlying analytical framework of competitive neutrality sits uneasily with the premises of social enterprise. At the practical level, we show how the qualitative cost-benefit balancing flowing from the public interest test embedded in the Australian competitive neutrality regime is quite different from the way social enterprises achieve their social objectives, despite some apparent initial similarities. This challenge arises from the implicit baseline of competitive neutrality, which tends to generate analyses that place efficiency values in competition with social, environmental or other ‘non-economic’ values. Articulating and responding to this challenge is important for the enhancement of opportunities for social enterprise to grow and strengthen, and thereby contributes to rising political and consumer demand for more sustainable business models.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.227
Teacher spread0.187 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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