Enriching social work research through architectural multisensory methods: Strategies for connecting the built environment and human experience
Bibliographic record
Abstract
Scholars have called for greater emphasis on the physical environment to expand social work research, policy, and practice; however, there has been little focus on the role of the built environment. Redressing this gap in the literature, this methodological paper explicates how four multisensory research methods commonly used in architecture—sketch walks, photography, spatial visualization, and mapping—can be used in social work research to create a greater understanding of the complex, interconnected, and multidimensional nature of built environments in relationship to human experience. The methods explored in this paper provide social work researchers with a methodological conduit to explore the relationship between the built environment and vulnerable populations, understand and advocate for spatial justice, and participate knowledgeably in interdisciplinary policy realms involving the built environment and marginalized populations.
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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 | 0.067 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".