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Record W7065676137

The Effect of Trust and Sense of belonging on Civic participation: a Comparative study between Korea and Canada

2021· dissertation· en· W7065676137 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsCivic engagementPoliticsPreferenceIdentity (music)General Social SurveySocial engagementSurvey data collectionPolitical efficacyAction (physics)
DOInot available

Abstract

fetched live from OpenAlex

This research examines the difference in civic participation between Korean and Canadian citizens in two categories – social engagement (participation in general social groups) and political activity by using the Korea Social Integration Survey (SIS) and the Canadian General Social Survey (GSS). According to the results of this study, Canadians show higher social engagement than Koreans, while Koreans exhibit higher political activity, showing higher combined civic participation between individual citizens. This heightened civic participation by Koreans also reflects a stronger sense of collectivism. The results of the analysis on the effect of civic participation for each country show that, in Korea, both trust and sense of belonging were positively associated while in Canada, trust was negatively associated, and sense of belonging more positively associated than in Korea. The difference between the two countries can be attributed to the negative association found in institutional confidence as well. In Canada, active participation in politics implies that civic participation is part of more forward-looking action that shows greater individual preference and intention in comparison to civic participation of Korean citizens influenced more by collectivism. As such, this research implies that Korea needs to enhance individual civic identity in order to overcome collectivism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0270.000

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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