Community Influence Capacity on Firms: Lessons from the Peruvian Highlands
Bibliographic record
Abstract
While much research has studied corporate management of stakeholders, this research focuses on the capacity of stakeholders to influence firms. Using a grounded theory research design, we draw on a comparative analysis of the relations between two neighbouring communities in the Peruvian highlands and the mining project that affects them. Our analysis suggests that control of resources and structural configurations are insufficient for explaining divergent actions and influence capacity, and highlights the role played by factors that we refer to as community vigour and the community’s pool of knowledge. We argue that these factors explain a community’s ability to develop an informed and shared interpretation of the situation in relation to firms and, therefore, to identify and carry out actions that will be more likely to influence firms to the community’s satisfaction. Thus, community vigour and pool of knowledge are additional sources of influence capacity. These findings contribute to the literature on stakeholder influence by providing a conceptual model that explains variance in stakeholder influence capacity that theories of resource dependence, structural position or network centrality do not fully explain.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".