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Record W4416007800 · doi:10.5465/amproc.2025.169bp

Small Business, Big Responsibility: The Social Responsibility Experience of Rural Small Business

2025· article· en· W4416007800 on OpenAlexaffabout
Julie Pitcher Giles, William Green

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSmall businessRural areaSocial responsibilityCorporate social responsibilityContext (archaeology)Rural sociologyRural management

Abstract

fetched live from OpenAlex

Though well-established in literature and practice, the experience of corporate social responsibility has rarely been examined in the context of the rural community, a valuable and unique setting often dominated by small business in both developed and underdeveloped economies. This qualitative study examined the experience of social responsibility among twenty-four small businesses throughout rural Newfoundland, Canada, and explored the perceived impact of the rural context on that experience. Findings contribute to a rich characterization of the social responsibility experience among rural small business owners and reveals that engagement in this type of behavior is not specifically linked to a deliberate effort to enhance business performance, rather, an overwhelmingly desire to support and enrich the community. Revelations of the benefits of such engagement, and a consistent experience of significant personal cost to these rural small business owners are particularly noteworthy. This research extends scholarly knowledge related to small business and rural social responsibility by deepening the current understanding of the experience in the rural context. Further, it offers recommendations on future policy directions related to social and economic development in rural communities, and to practical considerations for rural small business owners in their engagement with their rural communities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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 designObservational
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
Published2025
Admission routes2
Has abstractyes

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