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Record W2943145050 · doi:10.1145/3290607.3313268

'In the Same Boat',

2019· article· en· W2943145050 on OpenAlexaff
Raquel Robinson, Elizabeth Reid, Ansgar E. Depping, Regan L. Mandryk, James Fey, Katherine Isbister

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsClosenessLeverage (statistics)Computer scienceSynchronization (alternating current)Human–computer interactionScheme (mathematics)Artificial intelligenceChannel (broadcasting)MathematicsComputer network

Abstract

fetched live from OpenAlex

Social closeness is important for an individual's health and well-being, and this is especially difficult to maintain over a distance. Games can help with this, to connect and strengthen relationships or create new ones by enabling shared playful experiences. The demo proposed is a game we designed called 'In the Same Boat', a two-player game intended to foster social closeness between players over a distance. We leverage the synchronization of both players' physiological data (heart rate, breathing,facial expressions) mapped to an input scheme to control the movement of a canoe down a river.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.091
GPT teacher head0.454
Teacher spread0.363 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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