Learning to practice social responsibility in small business: challenges and conflicts
Bibliographic record
Abstract
Purpose The purpose of this paper is to address issues of practicing social responsibility (SR) in small business, where SR implementation challenges are unique. The discussion examines the difficulties encountered by small business owners adopting SR practices, and the various strategies they learned in the process. Design/methodology/approach A total of 23 small business owner‐managers located in Western Canada were interviewed in‐depth, individually, and in groups. Group interviews were useful for validating and extending the themes and contradictions that arose in individual interviews, particularly in identifying the most common SR challenges and frustrations, and to compare individuals' learning patterns and diverse strategies of response. Findings The paper findings show that owners learned SR by working through three main areas of challenge within everyday sociomaterial practices: positioning SR commitments and affiliations; balancing diverse stakeholders with SR ideals and costs; and negotiating value conflicts within SR practice, as part of “becoming” a particular enterprise of SR engagement. Originality/value The paper suggests that SR may be most fruitfully studied by examining the traces of the networks, linkages, and boundaries formulated through everyday interactions, focusing not just on the social networks and information exchange among humans, but more deeply on the sociomaterial networks within which new practices such as SR emerge. Second, the paper underscores the importance of conceptualizing SR “learning” more in terms of practices that emerge through challenge and conflict than in acquisition and application of new knowledge and attitudes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".