Stresscapes: validating linkages between place and stress expression on social media
Bibliographic record
Abstract
Understanding how individuals and groups perceive their surroundings and how different physical and social environments may influence their state-of-mind has intrigued re-searchers for some time. Much of this research has focused on investigating\nwhy certain natural and human-built places can engender specific emotive responses\n(e.g. fear, disgust, joy, etc.) and, by extension, how these responses can be considered in placemaking activities such as urban planning and design. Developing a better understanding of the linkages between place and emotional state is challenging in part because both cognitive processes and the concept of place are complex, dynamic\nand multi-faceted and are mediated by a\nconfluence of contextual, individual and social processes. There is evidence to suggest that social media data produced by individuals in situ and in near real-time may provide novel insights into the nature and dynamics of individuals’ responses\nto their surroundings. The explosion\nof user-generated digital data and the sensorization of environments, especially in urban settings, provide opportunities to build knowledge of place and state-of-mind linkages that will inform the design and promotion of vibrant placemaking by individuals and communities. In this paper we present a novel study, to be undertaken\nthis summer within the Greater Toronto\narea in Canada, with 140 recruited participants who are frequent, geo-tagging, Twitter users. The goal of the study will be to assess emotional, acute and chronic stress experienced in urban built-environments and as expressed during\ndaily activities. An existing automated semantic natural language processing tool will be validated through this study, and it is hoped that the methodology developed can be extrapolated to other urban environments as well, with a second validation study already planned to take place next year in London, United Kingdom.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".