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Record W2073417241 · doi:10.1145/1979742.1979491

RepliCHI - CHI should be replicating and validating results more

2011· preprint· en· W2073417241 on OpenAlex

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

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsGoogle (Canada)
Fundersnot available
KeywordsReplication (statistics)NoveltyCornerstoneComputer scienceValue (mathematics)PsychologySocial psychologyBiology

Abstract

fetched live from OpenAlex

The replication of research findings is a cornerstone of good science. Replication confirms results, strengthens research, and makes sure progress is based on solid foundations. CHI, however, rewards novelty and is focused on new results. As a community, therefore, we do not value, facilitate, or reward replication in research, and often take the significant results of a single user study on 20 users to be true. This panel will address the issues surrounding replication in our community, and discuss: a) how much of our broad diverse discipline is 'science', b) how, if at all, we currently see replication of research in our community, c) whether we should place more emphasis on replication in some form, and d) how that should look in our community. The aim of the panel is to make a proposal to future CHI organizers (2 are on the panel) for how we should facilitate replication in the future.

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)0.020
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.569
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.000
Open science0.0030.013
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 head0.512
GPT teacher head0.465
Teacher spread0.046 · 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

Quick stats

Citations59
Published2011
Admission routes1
Has abstractyes

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