Developing Heads of Department to Manage Quality: An examination of Performance Management Frameworks
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
The chapter addresses a number of questions related to the impact of performance management policies and processes upon the delivery of good quality teaching and learning; examining the advantages that can be gained from the analysis of good quality data but also recognising some of the ‘pit-falls and bear-traps’ that may be encountered if decision-makers do not take into account the limitations of an over reliance upon performance metrics. The chapter provides a short historical context to the development of a performance culture in the UK before considering how performance has been both measured and treated in other countries, discussing examples from the Netherlands, Pakistan, Canada, the USA and China that sought to introduce frameworks for the most effective disbursement and use of available funds for Research purposes. The chapter goes on to review some of the formal approaches that have been developed to support performance management and monitoring within organisations, including the Balanced Scorecard, Total Quality Management (TQM) and the European Foundation for Quality Management (EFQM). The conclusion to the chapter starts with an examination of the introduction of the Teaching Excellence Framework (TEF) in the UK, with the resultant need to collect robust performance data to support the metrics already supplied as part of statutory data returns. Finally considering the roles that individuals can play in the delivery and maintenance of quality management systems that enhance institutions and do not act as proverbial mill-stones.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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