Empathic Cultural Mapping: Little data, big data, knowledge transfer and exchange
Bibliographic record
Abstract
IntroductionA healthy city is one that continually creates and improves psychosocial and social environments and expands community resources allowing people to develop to their maximum portential. The role of SEDoHs is incontestable, yet we continue to face many of the same SEDoH-related problems despite what we know.
 Objectives and ApproachThis project presents the idea of "Empathic Cultural Mapping" (ECM). ECM is an interactive story map which brings together vignettes taken from individual stories curated with "big data" derived from places such as Statistics Canada, the City of Calgary, and library holdings at the University of Calgary. ECM seeks to challenge users to re-imagine long held constructions around sectoral, and disciplinary driven interpretations and categorizations of lifestyle, consumption, health, and the environment. ECM seeks to encourage knowledge users from multiple sectors to think beyond what is known and to consider what might be possible.
 ResultsECM is a creative interactive undertaking. In developing ECM, a range of creative research processes have been used to record and tell the stories of a small group of newcomers (defined as those who migrate, seek refuge, or claim asylum in Canada) and position these within large data. A desire to improve the health and wellbeing of individuals and communties through opening processes of dialogoue between local government, non-government organizations, communitites, and individuals lies at the heart of this project. Knowledge and sense-making are key features of individual and community empowerment within the ECM and are viewed as powerful stimuli for change as well as powerful allies for health and a buffer against its threats.
 Conclusion/ImplicationsECM creates, shares, and brings together individual stories and 'big data'. It identifies needs that impact health in the everyday. It seeks to improve awareness of the world around us. It encourages people to communicate their experiences. Finally, it achieves its goals by using creative processes.
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.
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 | 0.002 | 0.002 |
| 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 | 0.004 |
| Open science | 0.004 | 0.002 |
| 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".