Bibliographic record
Abstract
Shareholders of a company must increasingly share power with other social actors that control access to critical resources. These social actors are stakeholders because they have stakes in firms’ operations, either through being affected by them or through being able to affect them. Stakeholders are embedded in networks of relationships in which resources are shared, combined, exploited or restricted, and informal governance modes emerge. Strategic maneuvering in stakeholder networks is critical for assuring a firm’s access to valuable resources and resulting performance. Managers deciding on the strategic course of a firm embedded in a stakeholder network face multi-dimensional problems with multiple causes. It is argued that a three-way integration of the resource dependence theory, social network analysis, and stakeholder theory yields important insights for managers on options of strategic maneuvering in stakeholder networks. We highlight previous attempts to integrate pairs of these theories. Building on Boutilier’s typology of stakeholder network structures, we describe emerging governance patterns, and propose a set of possible moves aiming to address strategic challenges in gaining access to resources controlled by stakeholders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".