Efficiency outcomes from space charging in UK higher education estates
Bibliographic record
Abstract
Purpose Higher education (HE) in the UK has recently suffered financial pressure due to reduced central funding, and the requirement to widen student access. The estate is typically the second highest revenue expense and is an obvious target for efficiency gains. Sector‐wide statistics show there are opportunities for improving efficiency and many influential bodies have advocated space charging as a way of achieving them. This paper aims to investigate space performance indicators for evidence that space charging improves space use efficiency. Design/methodology/approach The approach used is a statistical analysis of space performance indicators for space charging and other higher education institution (HEIs). Findings Approximately one‐quarter of HEIs in the UK operated space charging in 2000‐2001 but only ten out of 31 space‐related performance indicators for the period 1998‐2001 suggest that increased efficiency results. Scrutiny of the background data shows they predominantly reflect differences in institutional wealth and activities, rather than space use management. Efficiency measures relating space to use provide no evidence of efficiency gains, suggesting that the application of charging as a space management tool is ineffective. Research limitations/implications The methodology does not reveal the reasons for the disparity between theory and results of space charging. Qualitative research into the application of charging systems is required to provide an explanation. Practical implications The conclusions are important for HE managers who are considering implementing expensive systems to improve space efficiency. The results also shed light on the usefulness of space performance indicators for HE estates. Originality/value Although there have been many assertions that space charging will improve space use efficiency in the HE sector, this research provides evidence to the contrary.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".