Applying Stakeholder Theory to the Management Functions
Bibliographic record
Abstract
Figure 4.1 illustrates the major management functions to which stakeholder theory and management strategies can be applied. In fact, stakeholders can influence, and be influenced by, ALL aspects of planned events, so this is merely a starting point. Subsequent sub-sections with diagrams look more closely at each of these functional areas and how they influence planned events. From the theoretical discussions presented so far in this book it should be clear that stakeholders are to be considered an integral part of event management and event tourism, not an isolated issue to be considered once in a while. This suggests that external stakeholder relations be a management function on its own, or tied to a position called something like ‘External Relations’. For internal stakeholders a different approach might be required, as each manager within the organization is going to have specific stakeholder issues to deal with on a continuous basis. Bringing these issues into one integrated approach will be the responsibility of the executive. This chapter also brings other theoretical perspectives to bear on stakeholder management. For example, starting with organizing and planning, we identify five themes for special consideration, each being informed by other theories. Strategies and projects links with institutional theory (e.g., how to become a permanent institution) and project networks including the political market square. Accordingly, these discussions provide a launching point integrating many theoretical perspectives on management.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".