Setting a course in corporate sustainability performance measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it