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Record W4245189763 · doi:10.31234/osf.io/bn24c

Field studies of psychologically targeted ads face threats to internal validity

2018· preprint· en· W4245189763 on OpenAlexaff
Dean Eckles, Garrett Adam Johnson, Brett R. Gordon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPersuasionField (mathematics)Face (sociological concept)PsychologySocial psychologySociologyMathematicsSocial science

Abstract

fetched live from OpenAlex

This is a comment on Matz, S.C., Kosinski, M., Nave, G., & Stillwell, D.J. (2017) Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences.This comment has been published as a letter to the editor: https://doi.org/10.1073/pnas.1805363115

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)0.304
metaresearch head score (Gemma)0.634
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score0.858

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3040.634
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0050.020
Scholarly communication0.0070.010
Open science0.0070.006
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0090.006

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 head0.387
GPT teacher head0.441
Teacher spread0.054 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations10
Published2018
Admission routes1
Has abstractyes

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