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Record W2205012351 · doi:10.1136/bmj.h4129

Prominent Canadian researcher loses libel case against documentary makers

2015· article· en· W2205012351 on OpenAlexaboutno aff
Owen Dyer

Bibliographic record

VenueBMJ · 2015
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentary evidencePolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

A formerly renowned researcher in the field of nutrition and immunology has lost his court bid to win damages from the Canadian Broadcasting Corporation over a 2006 documentary. The programme alleged that Ranjit Kumar Chandra never conducted published studies into the immune benefits of baby formula and other studies that found a protective effect of multivitamins on cognition and infection in elderly patients. Ranjit Kumar Chandra published more than 200 articles while working at the Memorial University of Newfoundland and gained international renown in the field of immunology and nutrition, receiving the Order of Canada for his work. But Chandra’s career began to unravel in 2000 when he …

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)0.018
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.962
Threshold uncertainty score0.498

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0340.015
Scholarly communication0.0240.008
Open science0.0060.004
Research integrity0.0380.048
Insufficient payload (model declined to judge)0.0310.014

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 head0.136
GPT teacher head0.422
Teacher spread0.286 · 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.

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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