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Record W2888591174 · doi:10.1177/1042258718801593

Successful Scaling in Social Franchising: The Case of Impact Hub

2018· article· en· W2888591174 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFranchising Strategies and Performance
Canadian institutionsUniversity of Ottawa
FundersEngineering and Physical Sciences Research Council
KeywordsFranchiseSocial entrepreneurshipBusinessCorporate governanceSocial identity theoryValue (mathematics)Identity (music)Dual (grammatical number)Creating shared valueScale (ratio)Collaborative governancePublic relationsKnowledge managementMarketingEntrepreneurshipSociologySocial groupCorporate social responsibilityPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Social entrepreneurs increasingly use franchising to scale social value. Tracey and Jarvis described how social franchising is like commercially-oriented franchising, but noted critical challenges arising from dual goals. We investigate a social franchisor that overcame these challenges and describe how the social mission became the source of business model innovation. We show that the social mission fostered a shared identity that guided the search for adaptations to the franchise model. The shared mission-driven identity created pressure toward (1) decentralized decision-making, (2) shared governance, and (3) a role for the franchisor as orchestrator of collaborative knowledge sharing among franchisees. Findings should help social franchisors avoid common pitfalls and suggest future research questions for social entrepreneurship and franchising scholars.

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.

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)0.003
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.310
Teacher spread0.288 · 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