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Record W4238524924 · doi:10.26434/chemrxiv-2021-7m4tw

Response process validity evidence in chemistry education research

2021· preprint· en· W4238524924 on OpenAlexaff
Jacky M. Deng, Nicholas Streja, Alison B. Flynn

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProcess (computing)CognitionPsychologyManagement scienceApplied psychologyComputer scienceEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Response process validity evidence can provide researchers with insight into how and why participants interpret items on instruments (e.g., tests, questionnaires). In the chemistry education research literature and in the social sciences more broadly, there has been variable use and reporting of response process aspects of studies. This manuscript’s objective is to support researchers in developing purposeful, theory-driven protocols to investigate response processes. We highlight key considerations for researchers who are interested in using cognitive interviews in their research, including: the theoretical basis for response process, collecting response process validity evidence through cognitive interviews, and using that evidence to inform instrument modifications.

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)0.717
metaresearch head score (Gemma)0.910
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 score0.283
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7170.910
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0110.013
Science and technology studies0.0060.023
Scholarly communication0.0170.019
Open science0.0070.013
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0190.004

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 head0.716
GPT teacher head0.652
Teacher spread0.064 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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