Putting the S-word back into Sustainability: Can we be more social?
Bibliographic record
Abstract
In an era dominated by climate change debate and environmentalism \nthere is a real danger that the important ‘social’ pillar of sustainability \ndrops out of our vocabulary. This can happen at a variety of scales from \nbusiness level through to building and neighbourhood level regeneration \nand development. Social sustainability should be at the heart of all \nhousing and mixed-use development but for a variety of reasons tends \nto be frequently underplayed. The recent English city riots have brought \nthis point back sharply into focus. The relationships between people, \nplaces and the local economy all matter and this is as true today as \nit was in the late 19th century when Patrick Geddes, the great \npioneering town planner and ecologist, wrote of ‘place-work-folk’. \nThis paper, commissioned from Tim Dixon, explains what is meant by \nsocial sustainability (and how it is linked to concepts such as social capital \nand social cohesion); why the debate matters during a period when \n‘localism’ is dominating political debate; and what is inhibiting its growth \nand its measurement. The paper reviews best practice in post-occupancy \nsocial sustainability metric systems, based on recent research undertaken \nby the author on Dockside Green in Vancouver, and identifi es some of \nthe key operational issues in mainstreaming the concept within major \nmixed-use projects. The paper concludes by offering a framework for the \nkey challenges faced in setting strategic corporate goals and objectives; \nprioritising and selecting the most appropriate investments; and measuring \nsocial sustainability performance by identifying the required data sources
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".