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Record W3012489818 · doi:10.1177/1206331220912160

The Making of Contentious Political Space: The Transformation of Hong Kong’s Victoria Park

2020· article· en· W3012489818 on OpenAlexaff
Chi Kwok, Ngai Keung Chan

Bibliographic record

VenueSpace and Culture · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsSpace (punctuation)Government (linguistics)Public spaceSocial movementRecreationRepresentation (politics)SociologyPolitical sciencePolitical movementMedia studiesPublic administrationPolitical economyLawEngineering

Abstract

fetched live from OpenAlex

Space plays a vital role in structuring and forming social movements into particular shapes—especially via its physical settings, the representation constructed through dominant and alternative discourses, and protesters’ spatial practices therein. Geographers and urban theorists have long argued that public space is of paramount significance to collective actions. Yet we know less about how a sustainable, manageable, and iconic public space for continuous movement mobilization is created. This article uses Victoria Park, an iconic public space of contention in Hong Kong, as a case to examine how a contentious political space is made. Through archival research, we demonstrate how the Defend Diaoyutai Islands Movement of the 1970s transformed the park from an “empty” recreational space to a political space. People’s political actions made this transformation of the spatial order possible. Nonetheless, the British colonial government re-policed the spatial norms of the space, which in turn regulated both the government and protesters. The study affords significant opportunities for thinking about the spatial constraints of contentious politics.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.438
Threshold uncertainty score0.872

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0000.001
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 head0.020
GPT teacher head0.292
Teacher spread0.271 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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