Constructing Educational Resilience: The Developmental Trajectory of Vulnerable Taiwanese Youth
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".