Volhoubare fasiliteitsbestuur in winkelsentrums in Pretoria
Bibliographic record
Abstract
Although sustainable facility management is increasingly gaining recognition in developing countries and is implemented in new buildings, in particular, hardly any information is available regarding sustainable practices in facility management that are applied locally in South Africa, and particularly in Pretoria. In this article, five key areas for sustainable facility management in shopping centres are investigated, namely energy consumption, water consumption, materials and resource management, internal environment quality management, and location management. This study also established which sustainable facility management strategies and methods are currently being applied and what perceptions property managers in shopping centres in Pretoria have regarding sustainable facility management. Questionnaires and one-on-one interviews with property managers and centre managers and owners were employed to obtain qualitative information such as the perceptions and knowledge of the respondents, as well as quantitative information such as quantities and percentages. The sample and data collected for the study are limited to shopping malls in Pretoria with a commercial area of 10 000m² or more, which yielded a total of 69 shopping centres. Completed questionnaires were returned by approximately a quarter of the total sample population, representing a lettable area of 765 835m2 and 1 663 stores. Nearly 90% of the respondents indicated that the ‘property’ management function is done internally, compared with over 94% that internally manage the ‘facility’ management function. It was found that sustainable facility management practices are being applied in shopping centres in Pretoria, but that there is a clear preference for widely applied practices that lead to financial savings. Practices that contribute to social and environmental objectives are applied to a much lesser extent, due to the perception that such practices do not result in financial savings or contribute to the management of the centres and are, therefore, regarded as less important. *This article is written in Afrikaans.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".