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.
Bibliographic record
Abstract
Abstract Voice-based interaction is experiencing a second wind through the advent of machine learning (ML) techniques, affordable consumer products and renewed work on natural language processing (NLP) and large language models (LLMs). A growing body of work is exploring how users perceive new forms of computer-generated voices from qualitative and quantitative angles. However, critical voices have called for greater rigour, especially in confirming the voice as a manipulated variable, i.e. manipulation checks. We present three case studies that highlight the value of investing in rigorous manipulation checks for HCI researchers and designers. We demonstrate the importance of testing assumptions, the need for care and reflection in the design of response options and measurement and the advantages of more exploratory approaches to understanding user perceptions of and user experiences (UX) with voice phenomena. Through these case studies, we raise awareness, empirically justify and critically assess the value of manipulation checks for voice UX research and beyond.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it