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Record W7165621175 · doi:10.18192/politika.8318

Between Stability and Suppression

2025· article· W7165621175 on OpenAlexaffabout
Graham Taylor

Bibliographic record

VenuePolitika – Undergraduate Journal of International Affairs Politics and Policy · 2025
Typearticle
Language
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCornerstoneCorporate governanceUnintended consequencesCivil societyCitizen journalismNarrativeState (computer science)ChinaSustainable development

Abstract

fetched live from OpenAlex

This paper examines the Golden Shield Project as a cornerstone of the people’s Republic of China’s censorship and surveillance architecture, analyzing its implications on state control, economic strain, and social unrest. While China nominally guarantees freedom of speech, its legal apparatus allows for sweeping restrictions on information, reinforcing a governance model grounded in centralized narratives and pervasive digital monitoring. The expansion of the domestic security apparatus following the 1989 Tienanmen square protests illustrates the state’s increasing reliance on surveillance to suppress dissent. However, this strategy carries substantial financial and societal costs: rising domestic security expenditures have paralleled an increase in public dissent. through a comparative analysis of Canada's governance framework, this paper highlights how China’s prioritization of suppression over reform entrenches a self-perpetuating cycle of unrest. drawing on historical patterns, insights from economic data, and case studies of protest movements, this analysis underscores the unintended consequences of censorship. It concludes by exploring alternative governance models rooted in transparency, participatory decision-making, and public trust as more sustainable pathways in the era of global interconnectedness.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.318
Teacher spread0.303 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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