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Record W4234844921 · doi:10.31234/osf.io/uq45c

Common Concerns with MTurk as a Participant Pool: Evidence and Solutions

2018· preprint· en· W4234844921 on OpenAlexaff
David Hauser, Gabriele Paolacci, Jesse Chandler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsTable (database)Participant observationData sciencePsychologyComputer scienceSociologyData miningSocial science

Abstract

fetched live from OpenAlex

In this chapter, we outline the common concerns with MTurk as a participant pool, review the evidence for those concerns, and discuss solutions. We close with a Table of considerations that researchers should make when fielding a study on MTurk

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)0.633
metaresearch head score (Gemma)0.811
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.367
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6330.811
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.008
Science and technology studies0.0140.018
Scholarly communication0.0150.026
Open science0.0100.016
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0310.007

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 head0.117
GPT teacher head0.294
Teacher spread0.177 · 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 designObservational
DomainMethods
GenreEmpirical

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

Citations133
Published2018
Admission routes1
Has abstractyes

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