An introduction to ‘Making the city “home”: Practices of belonging in Pacific cities’
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".