Bibliographic record
Abstract
Previous articleNext article FreeThank You to Our ReviewersPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreThe editors wish to thank the following reviewers who gave so generously of their time to assist in the publication of this issue of the Journal of the Association for Consumer Research.Jennifer Argo, University of AlbertaHaipeng (Allan) Chen, University of KentuckyUtpal Dholakia, Rice UniversityHeather Dretsch, North Carolina State UniversityChristoph Fuchs, Erasmus UniversityKelly Herd, University of ConnecticutSteve Hoeffler, Vanderbilt UniversityDonna Hoffman, George Washington UniversityLaura Kornish, University of ColoradoCait Lamberton, University of PittsburghDonald Lichtenstein, University of ColoradoRavi Mehta, University of IllinoisMartin Mende, Florida State UniversityThomas Novak, George Washington UniversityBernd Schmitt, Columbia UniversityChristophe van den Bulte, University of PennsylvaniaMonica Wadhwa, INSEADKatherine White, University of British ColumbiaManjit Yadav, Texas A&M UniversityMin Zhao, University of Toronto Previous articleNext article DetailsFiguresReferencesCited by Journal of the Association for Consumer Research Volume 4, Number 3July 2019Consumer Response to Big InnovationsGuest Editors: Page Moreau and Stacy Wood Sponsored by the Association for Consumer Research Article DOIhttps://doi.org/10.1086/703698 © 2019 the Association for Consumer Research. All rights reserved.PDF download Crossref reports no articles citing this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.302 | 0.311 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".