Subjective and objective measures of organizational performance: An empirical exploration
Bibliographic record
Abstract
Introduction Governments around the globe now seek to judge the performance of their public services. This has given rise to the introduction of a range of complex and sophisticated regimes to provide information to politicians, managers and the public on organizational success or failures. Examples include an index of measures of performance of Chinese cities (China Daily 2004), the Comprehensive Performance Assessment in English local government (Audit Commission 2002), the Government Performance Results Act 1992 in the US, the Service Improvement Initiative in Canada, the Putting Service First scheme in Australia, Strategic Results Area Networks in New Zealand, Management by Results in Sweden, and Regulation of Performance Management and Policy Evaluation in the Netherlands (Pollitt and Bouckaert 2004). Researchers have increasingly turned their attention to public service performance (e.g., see the Symposium edition of Journal of Public Administration Research and Theory 2005 (Boyne and Walker 2005), on the determinants of performance in public organizations). Despite such progress, a persistent problem for public management researchers and practitioners has been the conceptualisation and measurement of performance. Previous research has shown that organizational performance is multifaceted (Boyne 2003; Carter et al . 1992; Quinn and Rohrbaugh 1983; Venkatraman and Ramanujuam 1986). This is because public organizations are required to address a range of goals, some of which may be in conflict. Consequently, public organizations are obliged to focus attention on multiple dimensions of performance. Boyne's (2002) review of these dimensions isolated five conceptual categories – outputs, efficiency, effectiveness, responsiveness and democratic outcomes.
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".