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Record W2026074865 · doi:10.1177/030981680408300101

Capitalism is making us sick

2004· article· en· W2026074865 on OpenAlexaboutno aff
Jeff Shantz

Bibliographic record

VenueCapital & Class · 2004
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismTollBeijingDeath tollLexiconCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPolitical scienceEconomic historyHistoryDevelopment economicsSociologyDemographyChinaMedicineVirologyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

For much of the first half of 2003, world attention from Hong Kong to Geneva was captured by news of a mysterious but deadly virus, similar to pneumonia, claiming lives in places as distant as Toronto and Beijing. As accounts of the virus's growing toll became regular occurrences, by February the term ‘SARS’ (Severe Acute Respiratory Syndrome) was part of the global lexicon. By the end of 2003, there had been around 8000 infections worldwide, and over 689 deaths related to SARS. In my hometown, Toronto, 43 people died of SARS in 2003.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0090.005
Open science0.0000.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.005

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 head0.014
GPT teacher head0.266
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations3
Published2004
Admission routes1
Has abstractyes

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