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Record W1689209334 · doi:10.3747/co.22.2448

Third-Party Online Surveys—Science, Selling, or Sugging?

2015· article· en· W1689209334 on OpenAlexaffvenue
Sasha Mazzarello, Mark Clemons, Ian D. Graham, Anil A. Joy, Sharda Jackson Smith, Carmel Jacobs

Bibliographic record

VenueCurrent Oncology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsUniversity of AlbertaUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedicineInternet privacyData scienceWorld Wide WebInformation retrievalComputer science

Abstract

fetched live from OpenAlex

Physicians receive many online survey requests from both academic and pharmaceutical sources [...]

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)0.150
metaresearch head score (Gemma)0.381
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.793

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.381
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.010
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.695
GPT teacher head0.596
Teacher spread0.098 · 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 designTheoretical or conceptual
DomainMethods
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

Citations7
Published2015
Admission routes2
Has abstractyes

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