Good Pandemic People: Citizenship and Ethical Striving During the COVID-19 Pandemic in Ottawa, Ontario
Bibliographic record
Abstract
When the COVID-19 pandemic first reached Ottawa, Canada in March 2020, the lives of nearly all residents were dramatically impacted. From the loss of jobs to the loss of loved ones, many experienced an intense period of loneliness, fear, and uncertainty. This thesis explores residents’ experiences of the pandemic in Ottawa and how these were shaped by the state’s response to COVID-19, namely its public health and economic response. It is based on fieldwork conducted during the first waves of COVID-19, which combined participant observation, interviews, and online observation. It begins by exploring how the state called on residents to take responsibility for public health, thereby enacting a certain type of citizenship, and the ethical striving of my interlocutors to become responsible. It then focuses on how state officials urged people to use their common sense at the limits of state advice and how my informants attempted to cultivate their ability to make safe decisions. Lastly, it analyzes how the introduction of CERB, a social program that targeted un- and underemployed Canadians, renewed public discourse about the purpose of welfare and how the program served as a technology of government that encouraged applicants to reflect on their receipt of the benefit.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".