MétaCan
Menu
Back to cohort
Record W2531314602 · doi:10.7202/1044293ar

Dear DTCA, Please Don’t Deceive Me, Don’t Play on My Fantasy

2018· article· en· W2531314602 on OpenAlexaffvenue
Jean‐Christophe Bélisle‐Pipon

Bibliographic record

VenueBioéthiqueOnline · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFantasyAdvertisingProduct (mathematics)Face (sociological concept)Subject (documents)PsychologyDirect-to-consumer advertisingMedicineSociologyArtBusinessComputer scienceLiteraturePharmacologyWorld Wide Web

Abstract

fetched live from OpenAlex

In the face of drugs advertising, what can we do? Is Direct-to-Consumer Advertising (DTCA) designed to be beneficial by objectively presenting a product or are they rather seeking to convince us that their product is patently good? Through this song, the goal is to live the experience of a patient who is subject to drug advertising, by integrating into the stanzas the main ethical issues raised by advertising.

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)0.003
metaresearch head score (Gemma)0.021
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 score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0060.011
Scholarly communication0.0100.008
Open science0.0010.003
Research integrity0.0050.018
Insufficient payload (model declined to judge)0.0280.024

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 head0.354
GPT teacher head0.547
Teacher spread0.194 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBioéthiqueOnlineSame topicPharmaceutical industry and healthcareFrench-language works237,207