A ‘Fruitless Obsession with Accuracy’: The Uses of Sensemaking in Public Sector Performance Management
Bibliographic record
Abstract
Sensemaking provides a framework for understanding and interpreting managers' working lives, based on a thorough critique of rational choice models. It refutes rational choice, and aims not to predict, prove or control but simply to explain: the making of sense. This article, based on the author's original prizewinning MBA thesis, presents research undertaken in 2005–2006 on the uses of sensemaking in understanding local government organisations' contrasting experiences of performance management systems: an internal system in Canadian city government, and an externally-imposed system in the form of the now abandoned Comprehensive Performance Assessment (CPA) in British local government. Key literature utilised includes social and organisational psychologist Karl Weick, British complexity theorists such as Ralph Stacey, and a literature on the politics of organisational behaviour (such as Guy Peters et al.). A critique of rational choice and summary of sensemaking precedes an account of phenomenological case study research across two countries, undertaken using ethnographic field methods. The author argues that using sensemaking to understand performance management systems provides a further imperfect means of making sense of inherent organisational equivocality. These systems are radically interpreted as devices for making sense of experience in organisations in which human behaviour cannot be expected to be forever accurately predicted and controlled. Conclusions with enduring relevance for practising managers are presented on the personal, tactical, methodological and philosophical ‘uses’ of sensemaking in managers' working lives.
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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 | 0.063 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.151 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".