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Record W2490335281 · doi:10.3167/trans.2016.060203

Blue Sky Matter

2016· article· en· W2490335281 on OpenAlexaff
Ole B. Jensen, Phillip Vannini

Bibliographic record

VenueTransfers · 2016
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsMobilitiesFocus (optics)Inscribed figureField (mathematics)Atmosphere (unit)Computer scienceSociologyPhysicsMeteorologyOptics

Abstract

fetched live from OpenAlex

In this article we present a theoretical framework for an understanding of the relationship between the material design of mobilities technologies and the multisensorial human body. Situating our work in the emerging field of “mobilities design” within the broader so-called mobilities turn, we focus on two very different aircraft types and their design (the large passenger jet Boeing 737 and the small propeller aircraft DHC-2) in order to explore the sensuousness of in-flight experience and atmosphere. We focus on the interior design of the aircraft as well as on their technical capacities, and end with a conclusion that offers a fl at ontological view of mobilities design. We argue that according the material design of mobilities technologies must be inscribed on equal terms with the sensing human subject if we are to claim that we have reached a better understanding of how mobility feels.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.281
Threshold uncertainty score0.940

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.2810.110

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2016
Admission routes1
Has abstractyes

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