Beyond Liberty: A Republican Perspective on COVID-19 Restrictions and the Politics of Freedom
Bibliographic record
Abstract
On 20 March 2021 thousands of tightly packed protestors marched from Hyde Park to Whitehall in central London. The object of their protest was the government’s ‘lockdown’, encompassing the closing of businesses and places of worship, restrictions on travel and requirements to wear facemasks. These regulations were deemed necessary to contain the spread of COVID-19 and preserve the National Health Service in the face of a ‘second wave’ of infections that began in late 2020. The rallying call for many of these protestors was ‘freedom’; it was on their signs and in their chants (Donnison, 2021). They believe that COVID-19 regulations threaten their liberty. A sentiment echoed by members of parliament like Steven Baker, who, presumably alluding to life in the German Democratic Republic, described these regulations as imposing a ‘checkpoint society, under extreme police powers’ (Parker et al, 2021). This is not an exclusively British phenomena either: protestors have demanded ‘freedom’ in the streets of Bucharest, Rome, Montreal and across the United States (Dettmer, 2021; Silverman, 2021). Politicians like Jim Jordan, American congressman, berated Anthony Fauci, the face of public health in the United States, over COVID-19 restrictions and demanded to know when Americans will ‘get their freedoms back?’(Brewster, 2021). This concern about freedom is easy to dismiss. It often seems like cheap rhetoric from far-right politicians and often gets compounded with bizarre conspiracy theories about the pandemic. Liberty has been keeping bad company. However, this doesn’t mean we can dismiss it out of hand. The return of the state has been one of the major stories of the pandemic. Regulations designed to contain the pandemic and preserve healthcare system undeniably interfere with many of the mundane elements of our day-to-day lives. This doesn’t mean that we are living some sort of dystopian ‘checkpoint society’, but perhaps the concern is not prima facie absurd. This chapter will give a fair hearing to what will be called the ‘libertarian objection’ to COVID-19 regulations. This argument claims that increased government regulations related to the pandemic limit what is permissible for a person to do and therefore has reduced individual liberty.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.065 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".