Evaluating Social and Environmental Issues by Integrating the Legitimacy Gap With Expectational Gaps
Bibliographic record
Abstract
This article adopts an issues management approach to corporate social responsibility (CSR) implementation. Issues evaluation, which is an integral component of issues management, can be conducted by using the concept of three expectational gaps (factual, conformance, and ideal gaps). However, the concept of expectational gaps suffers from an ambiguity that limits its application to issues evaluation. The legitimacy gap concept is used in this article to clarify the ambiguity surrounding expectational gaps. The study thus develops a four-gap framework for conducting a quantitative issues evaluation. This framework is applied to six social and six environmental issues in the context of the forest products industry in the Northwest United States by means of a survey of 278 society and 94 industry respondents. Results empirically demonstrate the existence of expectational gaps and also provide insights into the nature of misalignment between societal and business perceptions along these social and environmental issues. Appropriate managerial responses are suggested to narrow or bridge different types of gaps.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".