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Record W2084388055 · doi:10.1002/meet.2011.14504801187

Moving forward: Conceptualizing comfort in information sources for enthusiast cyclists

2011· article· en· W2084388055 on OpenAlexafffund
Jonathan Dorey, Catherine Guastavino

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsCentre for Interdisciplinary Research in Music Media and TechnologyMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPronounContext (archaeology)Coding (social sciences)Theme (computing)Content analysisComputer scienceSelection (genetic algorithm)PsychologySociologyLinguisticsWorld Wide WebArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

Abstract This research aims to identify how the notion of comfort in the context of bicycling is conveyed in an American bicycling magazine and online forum. An iterative approach, comprising of a content analysis and linguistic discourse analysis aims to go beyond the generally‐accepted definition that focuses on vibrations, and identify the different concepts and typical situations relevant to study comfort for enthusiast cyclists. Are discussed the selection criteria for the magazine and online forum, the development of the coding protocol (including the final operational definition of each concepts) and the method for retrieving and analyzing online forum posts. A quantitative analysis, looking at the number of occurrences for each concept and theme, combined with a qualitative analysis of pronoun use, positive and negative descriptors, and opposing statements shows a complex link between the cyclist, the bicycle, and the environment. The behaviour of the bicycle, environmental factors, what the cyclists thinks and feels as well as the goal of the ride affect how comfort is conceptualized.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations6
Published2011
Admission routes2
Has abstractyes

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