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Record W4399879392 · doi:10.55016/ojs/ajer.v50i1.55038

Academic Resilience: A Retrospective Study of Adults With Learning Difficulties

2004· article· en· W4399879392 on OpenAlexvenueno aff
John G. Freeman, Shari A. Stoch, Janet S. N. Chan, Nancy L. Hutchinson

Bibliographic record

VenueAlberta Journal of Educational Research · 2004
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationResilience (materials science)Academic achievementDevelopmental psychology

Abstract

fetched live from OpenAlex

This article reports qualitative analyses of two sets of retrospective interviews with adults with learning difficulties. The purpose of the study was to examine the high school experiences of these adults from a holistic perspective to understand possible factors that contributed to one group staying in school and the other group leaving school early. One set of interviews was conducted with adults who had returned to complete high school at an adult learning center (the late successful group). The second set of interviews was conducted with the early successful group, adults who had completed high school during adolescence. Interview questions focused on interests, friends, and general aspects of the high school experience. Analyses yielded three themes: intrapersonal support, interpersonal support, and institutional support. These data suggest that schools might act in a number of ways to counter the high rate of early leaving by adolescents with learning disabilities, including building strong teacher-student relationships, using students' interests to develop curricula and structured activities, and fostering a sense of purpose.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.474
Teacher spread0.424 · 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 designObservational
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

Citations14
Published2004
Admission routes1
Has abstractyes

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