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Record W2600675946 · doi:10.3138/jcfs.42.3.369

Constructing Educational Resilience: The Developmental Trajectory of Vulnerable Taiwanese Youth

2011· article· en· W2600675946 on OpenAlexvenueno aff
En-Ling Pan, Chin-Chun Yi

Bibliographic record

VenueJournal of Comparative Family Studies · 2011
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedPsychological resilienceDevelopmental psychologyPsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This study contends that the growth trajectory of disadvantaged youth does not need to follow the expected negative path. Diversified developmental patterns may be observed due to the resilience acquired in the process. To consider the cultural background of Taiwan, we selected five parent-youth dyads from economically poor families and explored possible mechanisms contributing to the educational success of children. Data are taken from in-depth interviews of Taiwan Youth Project (TYP). Drawing from memories of both parent and youth on how family practice and family relations contribute to resilience, we are able to document that educational resilience is the key to alter the negative developmental course. Among various individual and family strategies examined, parents’ high aspiration and expectation of children’s educational achievement is found to be the most pronounced factor leading to the positive outcome in terms of educational mobility. Analyzing the retrospective accounts of both generations, it is clear that parental expectation needs to be well perceived and accepted by children in order to achieve its goal. Furthermore, supportive parenting, actively monitoring children’s homework since young, and providing better educational resources are conducive to constructing educational resilience. The implication of shared educational norms across different social classes is briefly discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.227
GPT teacher head0.442
Teacher spread0.215 · 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.

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

Citations17
Published2011
Admission routes1
Has abstractyes

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