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Record W2967889582 · doi:10.1145/3342775.3342795

"I don't know what you're talking about, HALexa"

2019· article· en· W2967889582 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
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPopularityUsabilityComputer scienceFoundation (evidence)World Wide WebHuman–computer interactionInternet privacyPsychologyPolitical science

Abstract

fetched live from OpenAlex

As Voice User Interfaces (VUI) grow in popularity in both the research and academic world, designers are met with new challenges in delivering on the promises of voice interaction. These promises depict a world where one can just speak to their devices, akin to HAL-9000; yet, existing usability challenges still leave many disappointed. These challenges often make or break the experience users have with VUIs. We argue that what we are missing is a foundation on which to build (and deliver) our promises: it is essential to build a foundation of VUI principles that can guide future designers in the development of voice interaction. We must address the lack of research in developing foundational VUI-specific guidelines that can aid designers in meeting the expectations and promises of true voice interaction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations51
Published2019
Admission routes1
Has abstractyes

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