MétaCan
Menu
Back to cohort
Record W3153802371 · doi:10.2196/26630

Authors’ Reply to: And Justice for All? There Is More to the Interoperability of Contact Tracing Apps Than Legal Barriers. Comment on “COVID-19 Contact Tracing Apps: A Technologic Tower of Babel and the Gap for International Pandemic Control”

2021· letter· en· W3153802371 on OpenAlexvenueno aff
Li Du, Vera Lúcia Raposo, Meng Wang

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typeletter
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsnot available
Fundersnot available
KeywordsContact tracingInteroperabilityCoronavirus disease 2019 (COVID-19)TracingInternet privacySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakEconomic JusticeComputer scienceMedicineWorld Wide WebPolitical scienceVirologyLawInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

We appreciate Crutzen's comments [1] on our analysis [2] of the technologic Tower of Babel, which analyzed the global functioning and interaction of COVID-19 contact tracing apps.

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)0.005
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.061
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0610.045
Insufficient payload (model declined to judge)0.0170.016

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 head0.083
GPT teacher head0.386
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJMIR mhealth and uhealthSame topicCOVID-19 Digital Contact TracingFrench-language works237,207