Evaluating assumptions of scales for subjective assessment of thermal environments – Do laypersons perceive them the way, we researchers believe?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People's subjective response to any thermal environment is commonly investigated by using rating scales describing the degree of thermal sensation, comfort, and acceptability. Subsequent analyses of results collected in this way rely on the assumption that specific distances between verbal anchors placed on the scale exist and that relationships between verbal anchors from different dimensions that are assessed (e.g. thermal sensation and comfort) do not change. Another inherent assumption is that such scales are independent of the context in which they are used (climate zone, season, etc.). Despite their use worldwide, there is indication that contextual differences influence the way the scales are perceived and therefore question the reliability of the scales’ interpretation. To address this issue, a large international collaborative questionnaire study was conducted in 26 countries, using 21 different languages, which led to a dataset of 8225 questionnaires. Results, analysed by means of robust statistical techniques, revealed that only a subset of the responses are in accordance with the mentioned assumptions. Significant differences appeared between groups of participants in their perception of the scales, both in relation to distances of the anchors and relationships between scales. It was also found that respondents’ interpretations of scales changed with contextual factors, such as climate, season, and language. These findings highlight the need to carefully consider context-dependent factors in interpreting and reporting results from thermal comfort studies or post-occupancy evaluations, as well as to revisit the use of rating scales and the analysis methods used in thermal comfort studies to improve their reliability.
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.
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 it