Stakeholder Models for Value Creation and Allocation
Bibliographic record
Abstract
Scholars of strategic management, entrepreneurship, and organization theory have recently sought to integrate theories that emphasize stakeholder interests with those on value creation and capture. In this Symposium, we propose to advance research on this integration through the presentation of scholarly papers by four authors, each of whom will present recent findings developed with co-authors on this topic. The papers will be discussed formally by two leading scholars in this domain. The purpose of the symposium is to support scholarly dialogue among participants, with the discussants, and in a question-and-answer session with attendees. We envision that this dialogue will advance frontier approaches to modeling how stakeholder contributions and claims can be discerned analytically. This advancement will enhance linkages between stakeholder theory and conceptualizations of value creation and of the distribution of value among claimants. Towards A Stakeholder-Oriented Framework on Value Creation and Allocation Presenter: Julien Jourdan; HEC Paris Platform Governance of Social Issues Presenter: Jiao Luo; U. of Minnesota Stakeholder Engagement for Sustainable Development: A Conceptual Model of Orchestration Presenter: Lite Nartey; INSEAD Beyond Primacy: A Stakeholder Theory of Corporate Governance Presenter: Pushpika Vishwanathan; U. of Amsterdam
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".