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Record W4297623935 · doi:10.23880/aeoaj-16000161

No Vax, No Tax. COVID-19 and Negative or Positive Liberty

2022· article· en· W4297623935 on OpenAlexaboutno aff
R Malighetti

Bibliographic record

VenueAnthropology and Ethnology Open Access Journal · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EconomicsMedicineVirologyInternal medicineOutbreak

Abstract

fetched live from OpenAlex

The efforts to legitimize or to oppose the policies responses to COVID-19 have placed the concept of freedom at the center of the contemporary political arena. The article intends to contribute to the debate, by reconsidering a minor classic in modern political theory: Isaiah Berlin's lecture "Two concepts of Liberty". This Inaugural Lecture, delivered before the University of Oxford in 1958, as well as the subsequent pamphlet published eleven years afterwards, discuss the notion of Liberty by examining two conceptualizations: Negative Liberty and Positive Liberty. Berlin's reasoning is inspiring because it refers to a series of actual topics: the role of the state; the differentiation between right and left; the totalitarian and totalizing ideologies; neoliberalist anti-political culture; the state of exception and of absolute sovereignty: the calls to social responsibility. Berlin himself declared the aims of his reflections on liberty by stressing the importance of the comprehension of the emergence of the despotic regimes of the twentieth century. I will then present Berlin's discussion by reproposing an old article I wrote in 1979 when, as a young student at McGill University, I was eager to clarify the relationship between political narratives and experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.000

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.

Opus teacher head0.127
GPT teacher head0.416
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAnthropology and Ethnology Open Access JournalSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207