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The potentials and perils of prosocial power: Transnational social entrepreneurship dynamics in vulnerable places

2022· article· en· W4220868797 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

VenueJournal of Business Venturing · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsMcMaster University
FundersUniversity of Edinburgh
KeywordsProsocial behaviorDisadvantagedIndigenousDominance (genetics)Context (archaeology)SociologyEntrepreneurshipSocial psychologySocial capitalLivelihoodPower (physics)PsychologyEconomic growthPolitical scienceGeographySocial scienceEconomics

Abstract

fetched live from OpenAlex

Social entrepreneurs can be powerful change agents for alleviating the suffering of the disadvantaged. However, their prosocial motivation and behavior frequently result in detrimental impacts on those they intend to support, especially when their operations span different socio-spatial contexts. We conducted a multiple comparative case study among 12 transnational social entrepreneurs of foreign, domestic non-indigenous, and local indigenous origin, who are seeking to improve the livelihoods of indigenous communities in rural Ecuador. We introduce the concept of prosocial power to social entrepreneurship research and demonstrate how it can work as a double-edged sword in the hands of transnationally embedded social entrepreneurs who operate in vulnerable places. Context-bound variations in social distance, bi-directional learning, reflexive impact measurement, and socio-spatial dominance were identified as being decisive for prosocial power to lead to positive or negative impacts on disadvantaged others.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.560

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 studies0.0010.000
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 head0.011
GPT teacher head0.215
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