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Record W2277803846 · doi:10.1177/1206331215579719

Walking Histories, Un/making Places

2015· article· en· W2277803846 on OpenAlexaffabout
Julia Aoki, Ayaka Yoshimizu

Bibliographic record

VenueSpace and Culture · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLifeworldSociologySituatedEthnographySpace (punctuation)Face (sociological concept)EpistemologyMode (computer interface)Gender studiesAestheticsAnthropologySocial scienceLinguistics

Abstract

fetched live from OpenAlex

This article is a methodological examination situated within a larger multisited project on the formation and regulation of communities of sex workers in Yokohama, Japan, and Vancouver, Canada, in historical and social discourse. Tracing the fragmented and elliptical histories of these communities, we are attentive to the potential for walking, and specifically walking tours, as an ethnographic method, a mode of historical engagement, and a means to reflect on our unfolding and shifting space–body relationships as we move across spaces of inquiry with varying levels of ease/tension. We seek to understand walking tours as a means and method to critically engage the histories that we seek to uncover and the absences we face in our attempts to uncover them—not only the social relations that constitute and are constituted by the space but also our own relationship to current communities that exist in the space—and the ways our lifeworld entanglements interfere with and give shape to our research endeavors. We problematize academic tendecies to situate lifeworld entanglements as secondary or superfluous to the research process. By tactically spatializing our personal experiences in a series of endnoted digressions, we make strange academic writing conventions of appropriate form and content.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.025
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.030
GPT teacher head0.317
Teacher spread0.288 · 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 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

Citations49
Published2015
Admission routes2
Has abstractyes

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