HeartBeat - An interactive installation to reflect the sentiments of Canadians during pandemics like Covid-19
Bibliographic record
Abstract
Social media has given citizens an avenue to express their views on various subjects in their personal lives, policies, and even a way to communicate with each other about their sentiments and emotions. This is key during a pandemic such as Covid-19 where the world is facing a global impact and the need for a pandemic-related public art framework has been sought globally by art societies and researchers to revitalize the society. However, due to the pace of this pandemic, most city art strategy papers require a framework for pandemic related public art especially in Toronto which has an agenda of moving towards becoming a smart city and public art should reflect that. This thesis investigates how might public art installations reflect the sentiment of smart communities in a pandemic. I designed 'HeartBeat', an interactive installation and visualization to reflect the emotions of citizens during the pandemic using Research Through Design and user-centered design approaches. The goal is to reflect the sentiment of Canadians during the current pandemic. HeartBeat uses tweets from Canada and visualizes the popular emotion groups during the pandemic period in an interactive installation. To evaluate HeartBeat, I conducted case study evaluation for various time periods and semi-structured interviews by selecting experts such as artists, designers, curators, policymakers, and data journalists. The contributions from HeartBeat could provide designers and artists exploring the pandemic to consider these design choices and methodologies; discussion shows the ways available to understand emotions of citizens during a pandemic in a smart city; detailed process design and technology stack architecture for pandemic related public art which could be used as public art frameworks during pandemics.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".