Using data matching to compare subjective assessments of daylighting environments between Singapore and Nanjing
Bibliographic record
Abstract
This study compares subjective evaluations of daylighting environments from two universities: the Singapore University of Technology and Design (SUTD) in Singapore and Southeast University (SEU) in Nanjing, China. Two hundred and twenty-nine students evaluated their instantaneous daylighting environments. Four representative daylighting predictors, horizontal illuminance, vertical illuminance, mean luminance of an entire scene and CIE Glare Index (CGI), were matched between two universities using a propensity score matching method. Eighty-eight participants, 44 from each university, were matched in terms of these four daylighting predictors. The results demonstrate that there are statistically significant differences in subjective assessments between these two locations. Under quantitatively similar daylighting environments, more participants at STUD reported adequate daylighting levels with a noticeable degree of daylight glare, as well as desires to decrease current daylighting levels. On the other hand, more participants at SEU reported inadequate daylighting levels with an imperceptible degree of daylight glare, as well as desires to increase current daylighting levels. One reason for subjective assessment differences might be dissimilar socio-environmental contexts, where the participants are acclimatized to different daylighting environments between Singapore and Nanjing.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".