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Record W6945203951 · doi:10.25316/ir-648

Intensities of mobility: Kinetic energy, commotion, and qualities of supercommuting

2016· other· en· W6945203951 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningMobilitiesEmbodied cognitionVariety (cybernetics)Work (physics)Order (exchange)

Abstract

fetched live from OpenAlex

This paper explores the intensities of long distance commuting journeys in order to understand how bodily sensibilities become attuned to the regular mobilities which they undertake. More people these days are travelling farther to and from work than ever before, owing to a variety of factors which relate to complex social and geographical dynamics of transport, housing, lifestyle, and employment. Yet, the experiential dimensions of long distance commuting have not received the attention that they deserve within research on mobilities. Drawing from fieldwork conducted in Australia, Canada, and Denmark this paper aims to further develop our collective understanding of the experiential particulars of long distance workers or supercommuters. Rather than focusing on the extensive dimensions of mobilities that are implicated in broad social patterns and trends, our paper turns to the intensive dimensions of this experience for supercommuters by developing an understanding of embodied kinetic energy, commotion and quality. Exploring how experiences of long-distance workers are constituted by a range of different material and bodily forces enables us to more sensitively consider the practical, technical, and affective implications of this increasingly prevalent yet underexplored travel practice.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.192
Teacher spread0.183 · 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 teacher head, 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

Citations0
Published2016
Admission routes1
Has abstractyes

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