Measure what matters: A blueprint for a sustainability culture diagnostic
Bibliographic record
Abstract
While many recognize sustainability as a valid risk management tactic, some CEOs are facing opposition to their sustainability initiatives. Given this challenging environment, we argue that it is critical that CEOs successfully execute their sustainability agendas to avoid criticism. Unfortunately, the complexities surrounding the execution of sustainability initiatives make achieving good sustainability performance difficult. As such, this article makes two small but critical contributions to improving organizational sustainability performance. First, we argue that if CEOs want to improve their organization’s ability to improve sustainability outcomes, they need to start by measuring their organization’s sustainability culture. Second, we leverage an empirically validated innovation culture measurement model to operationalize a sustainability culture diagnostic tool that CEOs can use to measure their organization’s sustainability culture. Finally, we provide preliminary guidance on how CEOs can use the sustainability culture diagnostic to improve their organization’s sustainability culture and performance.
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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 | 0.064 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".