Applying asset‐based community development as a strategy for CSR: a Canadian perspective on a win–win for stakeholders and SMEs
Bibliographic record
Abstract
In the December 2006 edition of Harvard Business Review, Michael Porter and Mark Kramer argue that by approaching corporate social responsibility (CSR) based on corporate priorities, strengths and abilities, firms can develop socially and fiscally responsible solutions to current CSR issues, which will provide operational and competitive advantages. We agree that an effective approach to CSR includes a mapping of strategy, risk and opportunity. However, we also caution that the identification of these to the exclusion of societal input may not be to the corporation's advantage. Instead, an investment in both strategic analysis and social capital can pay off from a social and an organizational standpoint. Compared with their larger counterparts, small‐ and medium‐sized enterprises (SMEs) frequently have stronger relationships with their internal and external stakeholders that foster the development of social capital. As such, we believe that the sector offers a unique opportunity to identify additional models and frameworks in order to approach a strategic CSR model as espoused by Porter and Kramer. This paper explores a case study of one Canadian SME that uses a community development framework called Asset Based Community Development (ABCD) for its CSR programming. Because ABCD relies heavily on the development and maintenance of social capital and can be utilized to attain set objectives, we propose that it provides a supplementary framework through which the arguments of Porter and Kramer can be expanded. In applying the ABCD framework for CSR, we can begin to establish a programme that supports strategy, integrates employees and stakeholders towards a common vision, and creates unique and sustainable alternatives towards the resolution of social and corporate goals.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".