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Record W3021073163 · doi:10.21810/strm.v11i1.271

Urban-Rural Mobility through the Lens of Food Documentary: A Case Study of “A Bite of China: Season Two”

2019· article· en· W3021073163 on OpenAlexvenueno aff
Yao Lu, Xiaoxiao Yang

Bibliographic record

VenueStream Interdisciplinary Journal of Communication · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChinaDignityModernization theoryResistance (ecology)Migrant workersModernityRural areaGeographySociologyEconomic growthSocioeconomicsPolitical scienceLawEconomicsEcology

Abstract

fetched live from OpenAlex

The combination of traditional Chinese food processing techniques and contemporary commercial food production forms the main topics of “A Bite of China: Season Two” (ABOC-2). Built upon the success of its first reason, ABOC-2 has achieved a record high TV rating among domestic audience and also made history by becoming the best-selling Chinese documentary overseas. By examining the individual experiences of migrant workers shown in ABOC-2, this paper discusses the important role of rural Chinese cuisine in maintaining the urban-rural mobility of contemporary China. We argue that one major storyline of ABOC-2, in which migrant workers maintain their inherent economic and social connections with their native countryside through cooking and consuming hometown dishes, sheds light upon migrant workers’ active resistance to the partially rational yet overly standardized ways of urban living. By showing respect to rural traditions and culture, ABOC-2 has successfully promoted the dignity of Chinese migrant workers and depicted their spiritual plight in contemporary China’s drive toward modernization.

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)0.003
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.009
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.359
Teacher spread0.335 · 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
Published2019
Admission routes1
Has abstractyes

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