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Record W2939978388 · doi:10.4324/9781351118828-6

Coming into a cultural inheritance

2019· book-chapter· en· W2939978388 on OpenAlexaboutno aff
Guanglun Michael Mu, Bonnie Pang

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsInheritance (genetic algorithm)GenealogyCultural inheritanceHistoryArtBiologyGeneticsLiterature

Abstract

fetched live from OpenAlex

Resilience is a culture- and context-specific process of socialisation. This chapter quantitatively compares the resilience process of Chinese Canadian and Chinese Australian children who are faced with various forms of structural constraints. The measurement model of Child and Youth Resilience remains psychometrically invariant across the two groups of children. The role of family support and cultural identity in the resilience process of the two groups does not show any statistical difference. Positive outcomes from the resilience process are seen in the strengthened social cohesion valued by multicultural societies. In this vein, the primary socialisation within the domestic milieu and the ideology of the multicultural state interpenetrate each other. Although the notion of resilience is traditionally rooted in the school of psychology, this chapter makes an attempt to develop a sociology of resilience through Bourdieu’s triad notions of habitus, capital, and field. Despite varied dynamics in different diasporic contexts, resilience can remain durable and transposable. Family upbringing and socialisation can enculturate Chinese children into a system of embodied dispositions responsive to challenge and change and empower these children with a set of capacities required for bounding back from adversities and participating in multicultural societies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.024
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.046
GPT teacher head0.393
Teacher spread0.348 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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