Research Ethics in HCI
Bibliographic record
Abstract
As interactive technologies evolve and reach into every aspect of modern life, research practices in human-computer interaction (HCI) have changed. The methodological and epistemological foundations of the field are shifting to reflect the diversity of contexts in which rapidly changing digital technology is being used. Alongside these changes, new ethical challenges emerge for the HCI community, both in terms of research ethics and responsible research and innovation. Open dilemmas include issues such as the shifting meaning of informed consent, anonymisation or privacy in an always-online world. The SIGCHI Ethics Committee has been established to look into the processes, practices and structures at SIGCHI venues to deal with such ethical dilemmas and how they can be addressed in a transparent, consistent and open way. This town hall style panel will be an opportunity to prompt community discussion and collect input into how we can further address these challenges.
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 | 0.263 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.099 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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.
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".