Subjective and objective measures of organizational performance: An empirical exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it