It Takes Two to Tango: From Community-Based Organizations to Corporate Community Relations
Bibliographic record
Abstract
While corporate-community relations are acknowledged by scholars, practitioners, and international associations today as crucial management issues, much remains to be done to deepen our understanding of these complex relations. In particular, while scholars have paid specific attention to these issues over the past decade, it unfortunately appears that local communities’ internal processes are under-studied to the detriment of firm-centered studies. However, understanding of these complex relationships cannot be achieved without a deep understanding of the processes and dynamics of both stakeholders. This gap raises our limited understanding of community processes and the influence of these processes on the corporate-community relations. For instance, our understanding of community-based organizations (CBOs) as community-driven arrangements that coordinate collective action and decision, remains limited. Relying on a 45-year longitudinal case study in the James Bay area of northern Québec, Canada, our study suggests three contributions to the literature. First, it analyzes and conceptualizes a four-stage process of CBOs allowing local communities to develop empowering structures: emergence, expansion, empowerment, refocus, and consolidation. Second, it defines and builds a typology of CBOs, based on their functions for local communities and their role in the scope of corporate-community relations: umbrella, implementation, representation, and joint institutions. Third, it discusses the influence of CBOs in structuring long-term corporate-community relations.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.070 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".