RepliCHI - CHI should be replicating and validating results more
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.532 | 0.815 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.029 | 0.046 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.014 | 0.034 |
| Insufficient payload (model declined to judge) | 0.031 | 0.027 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".