Lighting for well-being: a revolution in lighting?
Bibliographic record
Abstract
A model of lighting quality proposed in the 1990s defined good lighting as that which balances the needs of humans, economic and environmental issues, and architectural design. The model made explicit what had long been implicit: Lighting is not just about seeing details. Good lighting provides for the needed level of visual performance, but also determines spatial appearance, provides for safety, and contributes to human health and well-being. Far from being a revolutionary proposal, lighting for everyday well-being has long been a goal of lighting recommendations. The question for today is how quickly we should incorporate new research findings in revisions of recommendations. This paper will address the knowledge base and the state of lighting recommendations for three aspects of interior lighting that contribute to health and well-being: areas of high luminance (about which much is known, but more to be learned); luminous modulation (flicker) (about which we have some knowledge); and, total daily light exposure (about which knowledge is weak, but suggestive). Appropriately, recommendations are most specific for those areas about which knowledge is strongest. Revisions should keep pace with evolving knowledge, but not run ahead.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".