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Record W6945053840 · doi:10.22029/jlupub-7124

Caring like a State: Politicizing Love, Touch, and Precarious Lives in the Time of COVID-19

2020· article· en· W6945053840 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitätsbibliothek Gießen · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsTransformative learningMetaphorRefugeeCare workGuardianImmigrationEthics of careQueer

Abstract

fetched live from OpenAlex

This essay builds on the extraordinary circumstances brought about by the COVID-19 pandemic to tease out some of the ways in which love has been played out politi-cally in relation to migration. In Canada, as elsewhere in the world, the pandemic suddenly rendered visible the oft-invisible care work traditionally performed by women, and now increasingly so by women of color and asylum seekers. Building on queer theorist Sara Ahmed s understanding of immigration policies as a form of con-ditional love, I investigate various processes of (de)politicization that occurred when love and care became politically mobilized in response to the health crisis. I use the love-body-care constellation as working points to tease out some disciplining and transformative possibilities brought about by love. After discussing Lauren Berlant s and bell hooks reflections on love, I then examine how the pandemic unexpectedly made visible, and sometimes challenged, the politics of touch, love, and care between state-sanctioned hierarchized bodies. While so doing, I notably unpack the guardian angel metaphor that was mobilized to speak of those doing care work, and especially those working as continuing care assistants for the elderly overwhelmingly asylum seekers and women of color in Quebec. Running through the discussion lie lingering existential, political questions: who cares (in both the practical and emotional under-standings of the term), and how do we care about each other with what political consequences?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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