Clever COVID-19, Clever Citizens-98: Critical and Creative Reflections from Tehran, Toronto, and Sydney
Bibliographic record
Abstract
Our world suffers. Some people suffer more than others. Since the first part of 2020, ours is justly described as a time of uncertainty, threat, and upheaval. In this article, we offer reflections threaded narratively, told from the specificity of our societal contexts in Iran, Canada, and Australia. What might we learn in the present and anticipated future from people living chronically within conditions of uncertainty and immobility and also those experiencing uncertainty and immobility for the first time? We argue that reflexive comparative analysis bridging social and visual analysis, anchored in embodied conditions of such people, offers a way to learn from responses to COVID-19 while also being an exercise in ethical research practice. This reflection builds on and extends from our scholarly collaborations that have been ongoing since 2015. Our title recognizes this specific virus as stealthy. Importantly, our choice of words identifies resident Iranians-whose experiences were the original impetuses for this paper, and whose lives provide its empirical basis (98 is Iran's country code)-as equally steely.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.063 | 0.052 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".