MétaCan
Menu
Back to cohort
Record W3092453517 · doi:10.1080/09581596.2020.1829549

Corporate contact tracing as a pandemic response

2020· article· en· W3092453517 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCritical Public Health · 2020
Typearticle
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsCarleton UniversityUniversity of VictoriaMcGill UniversityYork UniversityUniversity of WindsorConcordia University
FundersFonds de Recherche du Québec-Société et Culture
KeywordsContact tracingCapitalismPower (physics)Public healthOppressionCorporate governancePublic relationsSociologyPolitical scienceCoronavirus disease 2019 (COVID-19)EconomicsLawManagementMedicine

Abstract

fetched live from OpenAlex

Since the start of the COVID-19 pandemic, a steady stream of propositions from tech giants and start-ups alike has furnished us with the idea that GPS- or Bluetooth-enabled contact tracing apps are a vital part of the pandemic response. This commentary considers these apps as ‘corporate contact tracing’, emphasizing the private-sector role that such developments imply. We first discuss corporate contact tracing’s potential to de-center the power of public health authorities. Then, using the frames of surveillance capitalism and disaster capitalism, we suggest how corporate contact tracing might feed the rise of corporate power in the public sphere. We question its capacity to address structural inequalities and to foster a social justice vision of public health. And, we wonder whether corporate contact tracing might intensify the effects of discriminatory design and algorithmic oppression. We conclude by calling for a discussion of this technology beyond questions of privacy and efficacy.

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.

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)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.214
GPT teacher head0.379
Teacher spread0.164 · 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