Empathy through data: Loneliness through the lens of data visualization.
Bibliographic record
Abstract
This research examines strategies aimed at fostering empathy through data visualizations. Loneliness experienced during the COVID-19 pandemic (during May, July and September 2020) is used as a case study to explore alternate ways of representing data. Along with ways to humanize data representation and curb Statistical numbing, this research uses metaphors to encode sensitive data to help visually represent people suffering loneliness in Ontario during the first wave of the COVID-19 pandemic. The research amalgamates ‘affect theory’ with concepts of ‘arithmetic of emotions’ and ‘compassion fade’ to try and create a solution for issues related to the ways in which we respond to sensitive issues. By employing the mixed methods of research for creation and iterative design development, this thesis comprises 1) a written document stating the research process; 2) a series of iterations leading to a data visualization for the first wave of the COVID-19 pandemic and 3) an interactive web-based data story.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".