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Record W4320404526 · doi:10.53841/bpsptr.2014.20.1.96

Using replication projects in teaching research methods

2014· article· en· W4320404526 on OpenAlex
Lionel Standing, Manuel Grenier, Erica A. Lane, Meigan S. Roberts, Sarah J. Sykes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychology Teaching Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsBishop's University
Fundersnot available
KeywordsReplication (statistics)PsychologyClass (philosophy)Mathematics educationComputer scienceMedical educationArtificial intelligenceBiologyMedicine

Abstract

fetched live from OpenAlex

It is suggested that replication projects may be valuable in teaching research methods, and also address the current need in psychology for more independent verification of published studies. Their use in an undergraduate methods course is described, involving student teams who performed direct replications of four well–known experiments, yielding results which were subsequently published online. Illustrative data are given for the one successful replication and three failures obtained, and practical suggestions are given for incorporating replication projects intoa methods course as an alternative to the usual term project. It is also noted that the published success rates of replication attempts appear to be higher for those studies that were performed as class projects.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0830.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.401
GPT teacher head0.669
Teacher spread0.268 · 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