Does the theory of stakeholder identity and salience lead to corporate social responsibility? The case of environmental justice
Bibliographic record
Abstract
Purpose The purpose of this paper is to review a possible link between the theory of stakeholder identity and salience (TSIS) and environmental justice and suggest a possible resolution. Design/methodology/approach This is a conceptual paper which also uses examples from industry. Findings The TSIS is a common management approach that helps companies determine stakeholders’ priority in building relationships and making decisions. The weakness of this theory is that it suggests that stakeholders lacking power, legitimacy and urgency be de-prioritized. This can lead to vulnerable populations’ interests being subjugated to those of more powerful stakeholders, leading at times to environmental injustice. This occurrence can jeopardize a company’s social license to operate. Therefore, it is suggested that TSIS be embedded in a situational analysis where the legitimacy and urgency criteria are applied beyond just stakeholders. Research limitations/implications Further research should look at the results of modifying the TSIS such that vulnerable populations are not de-prioritized. Practical implications This paper provides a way for organizations to be more cognizant of vulnerable populations and include them in decision-making to help avoid situations of environmental injustice. Social implications If organizations can recognize the impact of their decisions on vulnerable populations and include them in the decision-making process, situations of environmental injustice might not occur. Originality/value This paper brings to light one weak aspect of a commonly used and well accepted theory and suggests a way to mitigate potential harm that at times may arise in the form of environmental injustice.
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".