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Record W2017059749 · doi:10.1080/04419057.2002.9674290

Sister-city Partnerships and Cultural Recreation: the Case of Scarborough, Canada and Sagamihara, Japan

2002· article· en· W2017059749 on OpenAlexaffabout
S.M. Shaw, George Karlis

Bibliographic record

VenueWorld Leisure Journal · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSisterRecreationGeneral partnershipPoliticsAllianceSociologyAction (physics)Economic growthPolitical scienceLawAnthropologyEconomics

Abstract

fetched live from OpenAlex

Sister-city partnerships have existed for over 200 years. The goal of sister-city partnerships is to bring people together to foster mutual understanding, and to develop mutual benefits through the sharing of knowledge and new opportunities. A review of literature depicts that a number of factors entice the existence of sister-city partnerships such as educational services, political action and cultural recreation. However, scant attention has been placed on the role cultural recreation plays in the existence of sister-city partnerships. The purpose of this study is to examine the role cultural recreation plays in the existence of the sister-city partnership between the former city of Scarborough, Canada and Sagamihara, Japan. It is argued that cultural recreation is an important part of this sister-city partnership as it is prevalent in the educational services and political action pursuits that govern the existence of the alliance of these two cities.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0450.007
Scholarly communication0.0050.002
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.067
GPT teacher head0.324
Teacher spread0.257 · 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

Citations8
Published2002
Admission routes2
Has abstractyes

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