Bibliographic record
Abstract
Transcultural Cities is an edited text containing a diverse and cross-disciplinary selection of case studies centred around place-making through transcultural processes.The 21 chapters focus on distinct and specific spaces where cultures meet, and the strategies taken as communities negotiate with and around each other.The key idea in this text is the concept of transculturalism, and the authors draw from Pratt (1992), among others, to note how places are changed through the different cultures acting and interacting upon them.This book ultimately seeks lessons from the case studies, and Hou in his introductory chapter outlines the ways in which transcultural spaces potentially make cities safer and more accessible for their increasingly diverse populations, creating more livable urban experiences and mutual understanding.The rest of the book is divided into five parts, each containing four chapters.The first, Placemaking at the Margins, presents a series of case studies: South Asian Muslims in Chicago, Brazilian restaurants in Tokyo, West African immigrants in Seattle, and Chinese traditional festivals in Yangon, respectively.These chapters examine the way marginalised communities -particularly immigrants -inhabit specific places and change the nature of those spaces.For example, in Chapter 2, Sen's case study shows how the community combines religious (prayer) practice with commercial (restaurant) use to produce a transcultural space that can be used for different, and often changing, purposes.In this sense, places that would otherwise be classified as "ethnic" spaces, which tend to exclusivity, instead invite and support multiple communities, inviting hybridities and transcultural identities and activities.The second part, Placemaking in the Space of Flows, is not dissimilar from the first.The chapters continue to examine the transcultural spaces produced by migrant communities: Korean students and small business owners in the Philippines, Chinese migrants in Sydney, Asian migrant workers in West Malaysia, and migrant workers in Sheffield.However, the focus here is on flows, in which the movement of diverse peoples and cultures into and out of the space is key in the changing nature of the place.In Chapter 8, for example, Chang and Foo's case study examines how migrant workers from a diversity of origins: Nepal, Indonesia, Thailand, Bangladesh, among others, interact with the Chinese and Malay community in Kampung Kanthan in p. 157.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".