Application of a systems approach to sustainable development performance measurement
Bibliographic record
Abstract
Purpose The purpose of this paper is to demonstrate how a systems approach can be used to facilitate the development of an organizational performance measurement system. Design/methodology/approach Based on a review of the literature, the paper introduces the implications for applying a systems approach to organizational performance measurement. To demonstrate the transition from theory to practice, a case study is provided to show how a sustainable development performance measurement system was developed at a Canadian electric utility. The case study involved extensive consultation with over 25 experts. Findings The paper finds that a systems approach is useful in developing the process and that a set of formal systems criteria is useful in developing the structure and content of a performance measurement system. These concepts are highlighted throughout the case study example. Research limitations/implications The case study section was based on findings from a single organization. Further work is required to validate the findings within other organizations. Practical implications The paper shows how a robust sustainable development performance measurement system may be developed at an electric utility. The overarching emphasis on integration of the system with the case utility's mainstream initiatives demonstrates that a performance measurement system must build on what the organization already has in place. The systems‐based approach and formal systems criteria used in the paper may be transferable to other organizations. Originality/value The paper shows that a systems approach provides both the structure and flexibility needed to guide the design, implementation, and evolution of a sustainable development performance measurement system within existing organizational infrastructure.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".