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Record W2344416611 · doi:10.31542/j.muse.265

Does the MMR vaccine cause autism? How uncritical acceptance of information can have disastrous consequences.

2015· article· en· W2344416611 on OpenAlexvenueno aff
Alana Vowel

Bibliographic record

VenueMacEwan University Student eJournal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsSkepticismMMR vaccineAutismScientific evidenceCriminologyPolitical scienceMedicineVaccinationPsychologyPsychiatryImmunologyMeaslesEpistemology

Abstract

fetched live from OpenAlex

One of the most damaging medical controversies in several decades has been sparked by the publication of a fraudulent paper in 1998 claiming that a link between autism and the administration of the MMR vaccine had been found. The crisis is proving to be very difficult to remedy, as vaccination rates plummeted and have still not fully recovered. Fueled by media attention and ill-informed celebrity spokespeople, this poorly conducted study has received dangerous amounts of attention and support. Even with ever-increasing scientific evidence against the claim, the lack of skepticism and critical analysis of research has resulted in children being put in danger of preventable diseases by leaving them unvaccinated.

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)0.046
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.022
GPT teacher head0.281
Teacher spread0.259 · 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
Published2015
Admission routes1
Has abstractyes

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