Research Ethics for HCI
Bibliographic record
Abstract
An ongoing challenge within the HCI research community is the development of community norms for research ethics in the face of evolving technology and methods. Building upon a successful town hall meeting at CHI 2017, this panel will include members of the SIGCHI Research Ethics Committee, but will be structured to facilitate a roundtable discussion and to collect input about current challenges and processes. The panel will pose open questions and invite audience discussion of best practices focused on issues such as cultural and disciplinary differences in ethical norms. There will also be discussion of how ethical issues are handled in SIGCHI paper submission and reviews, processes for how we might create and disseminate ethics resources, and regulatory issues such as the recent IRB changes for U.S. institutions.
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 | 0.341 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.020 | 0.047 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".