Bibliographic record
Abstract
Purpose The purpose of this paper is to present situational, goal, and implementation diagnostic questions to guide the early stages in the development of a corporate sustainability performance measurement system (SPMS). Design/methodology/approach The paper highlights that measuring corporate sustainability is a complex problem. It argues that significant time must be devoted to defining sustainability in the corporate context, surveying the internal and external environments in which the corporation operates, establishing goals and objectives for the SPMS, identifying how the SPMS will be used, and identifying resource needs at the very beginning of the process to create a SPMS. Key questions that must be addressed in each of these areas are highlighted and discussed. Findings The situational, goal, and implementation diagnostic questions will help decision‐makers to structure thinking and discussion around the key issues that all meaningful corporate SPMS will need to address. The diagnostic questions will help corporate decision‐makers understand their current situation, the challenges in developing a robust SPMS, the desired end state, and the options available. Research limitations/implications The diagnostics are conceptual models and it is recognized that there is no optimal set of questions that will apply to all cases. With that in mind, the paper notes opportunities for additional research. Originality/value The diagnostics focus attention on the often neglected early stages of developing a corporate SPMS. They offer a novel approach to highlighting the key questions that must be addressed at the very beginning of the process. The diagnostics will be of interest to both researchers and practitioners 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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".