Bibliographic record
Abstract
Purpose The purpose of this paper is to present an approach for guiding the evolution of a corporate sustainability performance measurement system (SPMS). Design/methodology/approach A review of published literature highlights the need for further research on the evolution of corporate SPMSs. Building on existing research, the paper presents a three‐phase approach for structuring the evolution of a corporate SPMS: planning for an assessment of an SPMS; conducting an assessment; and following up on the results of the assessment. Key issues that must be addressed in each phase are highlighted and discussed. Findings The approach presented in the paper will help guide decision‐makers through the process of reviewing and updating their corporate SPMS. The guidelines will provide needed insight into the challenges and opportunities associated with the evolution of a corporate SPMS. Research limitations/implications The approach presented in the paper is a conceptual model. Opportunities for further research are highlighted in the paper. Originality/value The paper focuses attention on the frequently overlooked process of reviewing and updating a corporate SPMS. The paper offers practical guidance, including an extensive set of assessment questions, related to the evolution of a corporate SPMS. The paper will be of interest to both practitioners and researchers in corporate sustainability performance measurement.
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 | 0.147 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".