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Record W1994726185 · doi:10.1108/13683040510602867

Designing sustainable development indicators: analysis for a case utility

2005· article· en· W1994726185 on OpenAlexaff
Cory Searcy, Stanislav Karapetrović, Daryl McCartney

Bibliographic record

VenueMeasuring Business Excellence · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Management Systems
Canadian institutionsUniversity of AlbertaAlberta Environment and Protected Areas
Fundersnot available
KeywordsPerformance indicatorProcess managementProcess (computing)Computer scienceSustainable developmentMainstreamOriginalityKey (lock)Risk analysis (engineering)BusinessMarketingQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to present and analyze a case study on the design of a system of sustainable development indicators for an electric utility. Design/methodology/approach The case study is based on collaboration with an electric utility and consultation with external experts. A six‐step process was used to create the indicators: conduct a needs assessment; conduct process planning; develop a draft set of indicators; test and adjust the indicators; implement the indicators; and review and improve the indicators. Findings The case study demonstrates how existing projects impact the process of developing indicators. It highlights that any system of indicators must be linked to the business planning process. It shows how this may be accomplished through a design based on a hierarchical approach that also illustrates linkages between the indicators and incorporates existing measures. Research limitations/implications The first three steps of the indicator design process have been completed. Research on the remaining three steps is ongoing. Practical implications Applying the principles of sustainable development has become an essential part of doing business. This paper illustrates how sustainable development indicators may be developed and integrated with existing business infrastructure at an electric utility. Originality/value Even in companies with strong corporate responsibility programs, a key challenge is to construct meaningful indicators that are integrated with mainstream business systems. Although it is recognized that each situation is unique, this paper provides insight into the development of indicators within existing corporate infrastructures.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.054
GPT teacher head0.242
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2005
Admission routes1
Has abstractyes

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