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Record W2526227510 · doi:10.1080/09502386.2016.1236394

Who needs Cantonese, who speaks? Whispers across mountains, delta, and waterfronts

2016· article· en· W2526227510 on OpenAlexaffabout
Yao Xiao

Bibliographic record

VenueCultural Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPraxisNarrativeSociologyGeographyGender studiesHistoryPolitical science

Abstract

fetched live from OpenAlex

This essay digs into the ‘dirtiness’ of cultural studies to prioritize praxis, or as the way my mountain folks collect summer lotus: putting our feet in the muddy water, and finding ways to get the stems underneath the smooth leaves. Using Cantoneseness as a case to locate such ‘dirtiness’, I draw on personal/familial histories as well as research projects to tell how conjunctures of Cantoneseness are politically and unevenly lived. This complex journey travels roughly from my childhood in the Yuebei mountains of northern Guangdong through my teenage and early adult years in the Pearl River Delta (PRD) to my current location as a temporary resident in the East Pacific port of Vancouver. I selectively emphasize and speak on three different locations: first, the rural inland Cantonese location of Yuebei mountains – the borderland and hinterland experiences of my family and myself living what the mountains would offer and how these have changed; second, the industrial superior Cantonese location of PRD in Shenzhen and Guangzhou – my personal experiences and identities as a migrant youth, combined with a case study including interviews with migrant peasant-workers and their children; and third, the western transnational Cantonese location of East Pacific waterfronts in Vancouver and Richmond – my personal engagement with social activism as an ethnicized, international student, combined with a narrative study including interviews with social activists with different Cantoneseness and migration routes. While this mix of whispers speaks in its own way towards using cultural studies with more seriously (global) space-sensitive and (social justice) praxis-sensitive approaches, the primary focus is more modestly on my autobiographical accounts and research efforts as a specific case: to show some strategic locations of ‘Cantonescape’ on the one hand, and to invite wider conversations around cultural-spatial politics on the other.

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 score0.214
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.006
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.055
GPT teacher head0.372
Teacher spread0.317 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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