Effect of dimming control on office worker satisfaction and performance
Bibliographic record
Abstract
This experiment was conducted in a mock-up office-space laboratory. One hundred and eighteen participants worked for a single day under one of four lighting designs. They had no control over the lighting until the latter half of the afternoon, when all participants were offered some form of individual dimming lighting control. During the working day participants performed a variety of simulated office tasks, as well as completing a number of questionnaires on topics such as mood, satisfaction, and discomfort.Results related to questionnaire outcomes were consistent and convincing. After lighting control was offered there were significant improvements in mood, room appraisal, lighting satisfaction, glare dissatisfaction, environmental satisfaction, satisfaction with performance, self-assessed productivity, and visual discomfort. Further, our results suggest that it is not control in itself that is important, but exercising control to achieve preferred conditions. Participants who made the biggest changes to lighting conditions after they were given control tended to register the biggest improvements in mood, satisfaction and discomfort outcomes; those who made little change registered no improvements in outcomes. Task performance results were more equivocal. On many tasks, performance did significantly improve after control was introduced, but we attribute these improvements primarily to known practice effects. We recommend field studies over the longer term to test whether mood and satisfaction effects persist, and whether performance effects emerge.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".