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Record W2007739699 · doi:10.1145/2702123.2702336

Supporting Subtlety with Deceptive Devices and Illusory Interactions

2015· article· en· W2007739699 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
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of TorontoAutodesk (Canada)
Fundersnot available
KeywordsComputer scienceHuman–computer interactionFocus (optics)Set (abstract data type)Modular designMAGIC (telescope)Data science

Abstract

fetched live from OpenAlex

Mobile devices offer constant connectivity to the world, which can negatively affect in-person interaction. Current approaches to minimizing the social disruption and improving the subtlety of interactions tend to focus on the development of inconspicuous devices that provide basic input or output. This paper presents a more general approach to subtle interaction and demonstrates how a number of principles from magic can be leveraged to improve subtlety. It also presents a framework that can be used to classify subtle interfaces along with a modular set of novel interfaces that fit within this framework. Lastly, the paper presents a new evaluation paradigm specifically designed to assess the subtlety of interactions. This paradigm is used to compare traditional approaches to our new subtle approaches. We find our new approaches are over five times more subtle than traditional interactions, even when participants are aware of the technologies being used.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.384
GPT teacher head0.490
Teacher spread0.106 · 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