Multi-period Model for Selection of Stakeholder Engagement Strategies of the Company
Bibliographic record
Abstract
The present study proposes a multi-period model for selection of the most suitable types of engagementstrategies of the company with different stakeholders in the context of uncertainty (risk). In the model considereda number of scenarios under which relationships of the company with the stakeholder groups vary periodically.For each scenario periodically predicted the dynamics of changes in the characteristics of such relations, andcalculated weighing coefficients of applicability of the type of engagement strategy of the company with eachstakeholder group. Coefficients obtained are reduced to integral coefficients based on which, using a generalizedcriterion that combines the expected value and the mean squared deviation, made the decision on the choice of aparticular type of engagement strategies of the company with each stakeholder. This approach allows to selectand rank the Pareto-optimal set of types of strategies and delimit the risk tolerance of the decision maker. Themodel also provides a method of selecting the most suitable type of strategy based on the expected utilitycriterion. The advantage of the proposed model is that it takes into account the risk tolerance of thedecision-maker.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".