Tracing the Shifting Materiality of ESG Issues: Insights from Media Attention
Bibliographic record
Abstract
We analyze ESG-related news coverage to examine media attention patterns as a reflection of stakeholders’ perceived salience of ESG issues at both the industry and firm levels, offering insights into the evolving nature of ESG materiality. Using longitudinal data visualization over an 11-year period, we show that media attention to ESG issues varies significantly over time and across firms within the same industry. While some issues receive consistent attention, others exhibit shifting patterns, signaling changing stakeholders’ perceived salience. Focusing on SASB-informed financially material ESG issues, we also show that perceived salience varies even among high-relevance topics. Some firms align with their industry’s patterns, while others diverge markedly, reinforcing the view that ESG materiality is both dynamic and firm-specific. These insights suggest that static ESG materiality assessment frameworks may be insufficient for informing long-term sustainability strategies or corporate disclosure practices. For investors, our results underscore the value of media-based ESG signals in complementing traditional materiality assessments. Acknowledging the evolving nature of ESG materiality is essential for firms and investors aiming to develop ESG strategies that respond to shifts in stakeholders’ perceived salience of ESG issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".