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Record W3096660935 · doi:10.1177/0020872820962202

Pandemic sex workers’ resilience: COVID-19 crisis met with rapid responses by sex worker communities

2020· article· en· W3096660935 on OpenAlexaff
Elene Lam

Bibliographic record

VenueInternational Social Work · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Government (linguistics)Psychological resilienceSex workSex workersInequalityEconomic growthPolitical science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SociologyCriminologyPsychologyMedicineEconomicsSocial psychologyVirologyDemographyPopulationHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has exposed the inequality of social support systems worldwide, revealing the gaps that further marginalize vulnerable people. Despite the fact that sex workers are adversely affected by the pandemic, they are excluded from government relief and protection programmes as well as health services. Sex worker communities have developed rapid response strategies to support their peers in overcoming these challenges. Sex worker organizations all over the world have been working alongside other groups and communities to advocate for income and health support for all, and an end to repressive policing and state-sanctioned violence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0150.007
Scholarly communication0.0070.005
Open science0.0010.014
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.047
GPT teacher head0.352
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), 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

Citations57
Published2020
Admission routes1
Has abstractyes

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