Reimagining the language of engagement in a post-stakeholder world
Bibliographic record
Abstract
Abstract Language matters in shaping perceptions and guiding behaviour. The term stakeholder is widely used, yet little attention is paid to the possibility that its use may inadvertently perpetuate colonial narratives and reinforce systemic inequities. In this article, we critically examine the limitations of the stakeholder concept and its ambiguity, normativity, and exclusionary implications. We emphasise the importance of using language that gives a voice to marginalised groups, promotes inclusion and equity, and fosters meaningful and reflexive participation in decision-making processes. In critiquing the use of the term and calling for alternative practices, we aim to contribute to the decolonisation of research norms and the creation of more inclusive and equitable societies. Therefore, rather than advocating a single alternative term, we suggest a focus on the people, places, and species affected by decisions, interventions, projects, and issues.
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 | 0.071 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.101 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".