IDENTIFYING INTERIOR SPATIAL DIMENSIONS ACCORDING TO USER PREFERENCE: AN ASSOCIATIVE CONCEPT NETWORK ANALYSIS
Bibliographic record
Abstract
This study proposed a fundamental technique for evaluating the preferences of interior space users by capturing their verbally expressed preferences and then determining word associations. To accomplish this, the Pajek visualization software for large network analysis was employed in conjunction with the USF Word Association dictionary to visualize the structures and network depths of the derived associative meanings. The generated associative words were then qualitatively categorized into taxonomic word groups to reveal 13 dimensions of perceived interior-environmental quality, as follows: House-related, Territorial, Impression, Activity, Active Element of Nature, Nature, Building Materials, Companion, Household Basics, Color, Location, Composition, and Time Period. A factor analysis was then conducted to sort the generated associative words according to Out- Degree Centrality/ODC score. These were validated into five factors that appeared to influence the comfort levels of interior space users. These five factors and 13 dimensions are useful as objective bases for determining the composition of adjectival pairs through the Semantic Differential (SD) method, which helps designers and architects evaluate interior space preferences.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".