“Let the Water Speak” Using Fictional Writing to Revisit Stakeholder Theories and Give a Voice to Invisibilized Stakeholders
Bibliographic record
Abstract
Understanding the dynamic relationships of the entities that have the most impact on an organization—or that the organization impacts the most—is at the core of stakeholder management approaches. In this article, we present an experiential exercise that provides a creative practical, low-overhead, discussion-oriented classroom activity to engage in a critical examination of the concept of stakeholders. This exercise is especially effective for the stakeholders usually invisibilized. Rather than relying on presenting stakeholder theory, this exercise uses fictional writing as a way for students to give a voice to water, a most often invisibilized stakeholder on an academic campus. The activity encourages reflection on the perception we hold toward certain stakeholders and aims to raise awareness toward the underrepresentation of some of them despite the centrality of their contribution to the organization. The exercise also enables students to grasp that there are limits when trying to speak on behalf of someone or something that structurally does not have a voice. This exercise can be used at the graduate level. Recommendations for adapting the exercise to the large classes are included.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".