Performance Measures and the Rationalization of Organizations
Bibliographic record
Abstract
This article focuses on rationalization, its dimensions, the possibilities of reasoned justification in the public sphere, and the technologies that would operationalize this. It does so through an analysis of the introduction of performance measurement in the Provincial Government of Alberta, Canada. We argue that performance measurement represents twin dimensions of rationalization: the pursuit of reason in human affairs, that is, the process of bringing to light the justifications by which actions and policies are pursued; and rationalization as the increasing dominance of a means-end instrumental rationality. The article illustrates how an initial enthusiasm by managers for the performance management initiatives was replaced with scepticism and cynicism. We show how the potential for reasoned justification was frustrated in practice, through a growing disparity between a discourse of reasoned justification and the practical operationalization of mechanisms of business planning and performance measurement. The search for reasoned justification and instrumental mastery are part of the same rationalization process, and these two contradictory, but inherently connected forces are an important explanation of the dynamics of managers' responses to organizational change.
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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 | 0.036 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.062 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".