Beyond Contractual Governance: A Configurational Exploration of Stakeholder Value Capture
Bibliographic record
Abstract
Community-firm contracting has emerged as a widely used tool in the extractive industries, aimed at ensuring local communities receive a share of the value generated from large-scale resource development projects. These agreements typically include provisions for local hiring, educational scholarships, and other community-based benefits. While they appear to promote more equitable value distribution practices, empirical evidence suggests that their outcomes may vary significantly. In light of these mixed results, this study seeks to explore the conditions under which community-firm agreements lead to more favorable outcomes for local communities. To address this, we apply a value-based strategy (VBS) lens to examine when community stakeholders are better able to effectively capture value from their relationship. To do so, we develop a novel longitudinal dataset of 141 Indigenous communities in Canada, each of which is located near a resource extraction project where a community-firm agreement has been established. Using fuzzy-set qualitative comparative analysis (fsQCA), we identify specific community configurations that are more likely to benefit from these agreements, as well as those where contracting may negatively affect community wellbeing. Our findings contribute to a deeper understanding of when community-firm agreements may successfully distribute benefits among community stakeholders, leading to more equitable value distribution. Theoretically, this research also sheds light on how stakeholder factors, such as bargaining power and other contextual factors, influence value capture outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".