A Modeling Methodology for Multiobjective Multistakeholder Decisions
Bibliographic record
Abstract
anagementsciencescurrentlydonotoffera systematic approach to model thedynamics and effects of multiple stake-holders’ objectives on corporate decisions. The pur-pose of this article is to introduce a structured qualita-tive methodology that provides researchers with ameans to systematically model, analyze, and comparecases of context-rich, idiosyncratic organizationaldecisions that involve multiple sets of objectives ofmultiple and divergent stakeholders.The multiobjective multistakeholder decisionmodeling methodology consists of a stepwiseapproach for inferring organizational priorities bymodeling organizational objectives hierarchies. Anobjectives hierarchy classifies related, more specificsubsets of objectives into higher level categories ofbroader, more general objectives in a hierarchical treestructure. In the modeling methodology, we combinequalitative and structured elements to achieve twotraditionally exclusive research goals: retain a highlevel of the decision’s complexity and simultaneouslyprovidemeansforsystematiccomparisonswithinoneor among several decision cases. With this methodol-ogy, we aim to broaden the empirical base of stake-holder theory by expanding its methodologicalarsenal.The modeling methodology is nontraditional inthat it links two formerly distinct streams of research:(a) multiattribute decision analysis and, specifically,the objectives hierarchies method from decision anal-ysis (Keeney, 1992; von Neumann & Morgenstern,1947;vonWinterfeldt,1987)and(b)recentdescriptivedevelopments in the stakeholder literature (Freeman,1984; Mitchell, Agle, & Wood, 1997). The objectiveshierarchies method creates tree structures that orga-nize the objectives of a decision maker into related
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".