A dramaturgical accounting of cooperative performance indicators
Bibliographic record
Abstract
Purpose Electric cooperatives may be seen as an alternative form of organizing in the shadow of investor-owned utilities. They are presumed able to meet financial challenges while simultaneously honoring cooperative principles of member-owners. This paper aims to investigate such a balancing act and conceptualize “key performance indicators” (KPIs) as a dramatic accounting discourse. Design/methodology/approach This paper uses a dramaturgical approach to cooperative performance accounting, and claims that KPIs are a simplification of a complex and shifting reality which they also socially construct. Data were gathered from annual financial reports and websites of rural electric cooperatives along with semi-structured interviews conducted with senior cooperative officials. Findings The cooperatives in this case study reported a huge number of KPIs. However, this paper reveals that the performance indicators serve impression management goals and operational demands rather than reporting on fulfillment of the “Seven Cooperative Principles” that are fundamental to the cooperative movement. Research limitations/implications Extant inquiry regarding electric cooperatives tends toward a positivist research approach and a realist worldview. This overlooks dramatic and critical possibilities of KPIs as a management construction project. Expanding beyond mainstream research, this paper calls attention to artistic production of knowledge and applies a qualitative framework to problematize accounting disclosures. Originality/value Prior KPI research has often been instrumental, looking for predictive evidence that KPIs have strategic value as a “tool” for organizations to attain competitive advantage. This paper introduces the notion that performance measures are theatrical, and applies this to rural electric cooperatives, an industry mostly ignored in the academic literature.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| 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".