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Record W4231108001 · doi:10.31219/osf.io/hrnwa

Are Replication Studies Infrequent Because of Negative Attitudes? Insights From a Survey of Attitudes and Practices in Second Language Research

2021· preprint· en· W4231108001 on OpenAlexaboutno aff
Kevin McManus

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsnot available
FundersPennsylvania State UniversityUniversity of Pennsylvania
KeywordsReplication (statistics)OriginalityEmpirical researchField (mathematics)Quarter (Canadian coin)PsychologyComputer scienceSocial psychologyBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Replication is a research methodology designed to verify, consolidate, and generalize knowledge and understanding within empirical fields of study. In second language studies, however, reviews share widespread concern about the infrequency of replication. A common but speculative explanation for this situation is that replication studies are not valued because they lack originality and/or innovation. To better understand and respond to the infrequency of replication in our field, 354 researchers were surveyed about their attitudes toward replication and their practices conducting replication studies. Responses included world-wide participation from researchers with and without replication experience. Overall, replications were evaluated as relevant and valuable to the field. Claims that replication studies lack originality/innovation were not supported. However, dissemination issues were identified: half of published replication studies lacked explicit labeling and one quarter of completed replications were unpublished. Explicit labeling of replication studies and training in research methodology and dissemination can address this situation.

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 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)0.002
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.584
GPT teacher head0.585
Teacher spread0.001 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations3
Published2021
Admission routes1
Has abstractyes

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