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Record W3111384558 · doi:10.3138/topia-003

COVID-19: An Essay in Keywords

2020· article· en· W3111384558 on OpenAlexvenueno aff
Penelope Ironstone

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyMeaning (existential)Isolation (microbiology)Order (exchange)PoliticsCoronavirus disease 2019 (COVID-19)TRACE (psycholinguistics)PandemicEpistemologyLinguisticsPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

This essay is inspired by what has become a staple of cultural studies, the parsing of keywords that appear and crystallize during moments of social, cultural, political and economic change. It takes as its starting point a methodology based in the examples of Williams’ (1985) Keywords: A Vocabulary of Culture and Society and Grover’s (1987) “AIDS: Keywords” in order to trace the ways that the language of COVID-19 has served simultaneously to make intelligible and obscure, reveal and cover over the divisions, contradictions and inequalities of the pandemic experience. Rather than attempting a dictionary-style entry system of keywords, which would imply a stability of meaning that might be excavated and understood, this essay animates certain keywords of the pandemic, which are bolded below, in order to situate, define and critique them. It looks to particular keywords tied to pandemic subjectification and the divisions these elicit and elide, such as the asymptomatic carrier, essential worker, self-isolation/inverted quarantine and vulnerable populations, as heuristics for a kind of conjunctural analysis of the COVID-19 pandemic.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.395
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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