Does the theory of stakeholder identity and salience lead to corporate social responsibility? The case of environmental justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it