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Record W4324086032 · doi:10.26686/wgtn.22261522

The power of habit and electoral participation of ethnic Chinese in New Zealand

2023· dissertation· en· W4324086032 on OpenAlexfundno aff
Jie Huang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
FundersMarsden FundVictoria UniversityChina Scholarship CouncilVictoria University of WellingtonFederation for the Humanities and Social SciencesMcMaster University
KeywordsImmigrationVotingTurnoutPoliticsDemocracyPolitical scienceEthnic groupDemographic economicsAffect (linguistics)Power (physics)Voting behaviorPolitical economySociologyEconomics

Abstract

fetched live from OpenAlex

The political integration of immigrants into their host country is in part a test of how well a democratic society copes with diversity. This thesis focuses on Chinese immigrants and their electoral participation in New Zealand. Specifically, it draws on the habitual voting theory to probe whether and how much immigrants’ pre-migration experiences affect their civic engagement in New Zealand. The habitual voting theory emphasises the footprint effect of early voting or nonvoting on a person’s subsequent electoral participation. It indicates that after several repetitions of participation or non-participation in their first few elections, most people persist in voting or abstaining irrespective of their evolving political experiences and attitudinal changes over the life-cycle. Accordingly, this thesis expects the presence or absence of pre-migration democratic experience to be a key determinant of Chinese voter turnout in New Zealand. Moreover, the influence of pre-migration political experience may be sizeable and enduring. It is likely to moderate the effects of immigrants’ post-migration experiences and their accumulated socio-political orientations, resulting in a persistent variation in turnout among New Zealand Chinese born and raised in different societies. This research further hypothesises the existence of habitual attitudes among immigrants. Political predispositions picked up early in the birthplace may endure after emigration, helping immigrants navigate New Zealand politics and shaping their voting choices. To examine the above theoretical expectations, this thesis employs various surveys and observational data with a sufficient sample size of New Zealand Chinese. The primary data source is the New Zealand General Social Survey (NZGSS). Multiple supporting sources of information such as the New Zealand Vote Compass Survey and the New Zealand Election Study are used to fill the gaps left in the NZGSS. The results obtained using a wide range of statistical methods confirm that initial political experience in the origin society leaves a strong footprint effect, leading to more continuity than change in how New Zealand Chinese view and participate in politics. A persistently low rate of turnout is found among those from marginally democratic or non-democratic societies. Early redistributive preferences appear to remain stable over time, making individuals from less egalitarian societies more likely to vote for centre-right political parties in New Zealand. These findings lend support to the habitual voting theory. Participatory habits acquired early in life are sticky and can be transferred from one political context to another. Without considering the power of habit, one cannot provide a complete explanation of immigrants’ electoral participation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.414
Teacher spread0.371 · 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 designObservational
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

Citations4
Published2023
Admission routes1
Has abstractyes

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