Disengaged and Disenchanted Adolescents: An Arts-Based Case Study
Bibliographic record
Abstract
The pursuit of creating valuable learning experiences for adolescents is a complex challenge. The purpose of this study is to explore the experiences of a group of disengaged and disenchanted adolescents. I identified a gap in the literature; youth are often only quoted for comments affirming that interventions have been beneficial. The present study seeks to fill this gap by representing as directly as possible the experiences of participants that relate to their motivation and engagement. To investigate the processes at work that contribute to the general decline in motivation that occurs in early adolescence (Gottfried, Flemming & Gottfried, 2001; Harter, 1981; Whitlock, 2006), and that for some result in definitive disengagement with formal education, I used art elicitation to facilitate the communication of various experiences of school by one group of urban self-identified disengaged and disenchanted adolescents. The data were analyzed using traditional qualitative methods, as well as poetic inquiry, which offers aesthetic representations of reflexive research methods and meditations on significant themes and topics in the data. The visual and written data provide rich information about contemporary experiences of secondary school for the disengaged and disenchanted adolescent participants and inform a discussion of recommendations and personal discoveries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".