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Record W7000310870

An examination of the self-perceptions of at-risk youth and their educators on competency measures of self-determination

2010· dissertation· en· W7000310870 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumContext (archaeology)Sample (material)PerceptionStress (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Abstract This study examines self-determination skills and opportunities among a group of at-risk youth in a Montreal-based alternative school program. Students and their educators were given questionnaires measuring skills on self-determination that addressed choice making, decision making, problem- solving, goal setting, and self-advocacy. Thirty-five students (15-18 years) transitioning from a Montreal area alternative school program to community settings participated in this study. This study documented important differences on the perceptions of students and educators on self-appraisals of capacity and opportunity for self-determination, with students rating themselves higher on capacity than their educators. Results indicated variability among the at-risk groups and educators for opportunities to practice self-determination. Factors such as self-reported learning difficulties/emotional and behavioural disturbances, and levels of stress were found to differentially influence ratings of self-determination. In the context of high stress the majority of our sample experienced multiple risk factors that crossed domains (home and school environments) implicating their school performance. These results have implications for curricula and policies that support self-determination, and they highlight the importance of home-school collaborations

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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