Politics of citizenship during the COVID-19 pandemic: what can educators do?
Bibliographic record
Abstract
As a once in a 100 years emergency, the COVID-19 pandemic has resulted in repercussions for the economy, the polity, and the social. Also, the ongoing pandemic is as much a teaching moment as it to reflect on the lack of critical citizenship education. The fault lines of the health system have become visible in terms of infection and death rates; the fault lines of the educational system are now apparent in the behavior of the citizens who are flouting the public health guidelines and, in certain cases, actively opposing these guidelines. The main objective of this commentary is to initiate a dialogue on the social contract between the state and the subjects and to see how education and educators can respond to the challenge of the new normal. It is contended that education under the new normal cannot afford to keep educating for unbridled productivity education under the new normal. It must have welfare, human connections, ethical relationships, environmental stewardship, and social justice front and center.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.054 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".