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Record W4286499107 · doi:10.1111/taja.12432

An introduction to ‘Making the city “home”: Practices of belonging in Pacific cities’

2022· article· en· W4286499107 on OpenAlexaff
Daniela Kraemer, Monika Stern

Bibliographic record

VenueThe Australian Journal of Anthropology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCONTESTContext (archaeology)SamoanExhibitionSociologyUrbanizationPoliticsGeographyPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Abstract It is estimated that by 2050, 50% of Pacific peoples will be living out their full lives in cities and towns throughout Oceania and around the world. Over the last 35 years, previous patterns of circular migration have been giving way to permanent urban settlers and to generations born and raised in urban places. These ‘urbanites’ demonstrate a firm commitment to urban living in both the present and the future. In the cities, Pacific people have been building roots and making the city ‘home’. This special issue focuses on some of the diverse practices Pacific urbanites employ in creating ‘home’, which we define as the context where they centre their social, cultural and economic worlds and the place in which they see themselves living out their aspirations and future life course. Practices range from the use of graffiti, sharing music, playing reggae, the role of artists and exhibitions, and mobile phone use. In doing so, this special issue contributes to a growing body of work focused on the ways urban‐based Pacific peoples contest essentialising stereotypes that deny their legitimacy and dismiss their urban experiences as less authentic. The collection of papers demonstrates how Pacific peoples are imagining and demonstrating their identities as Pacific urbanites reflecting their commitment to an urban Pacific, national, cultural and political belonging.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.386
Teacher spread0.338 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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