An Integral Analysis of Labeling, Inclusion, and the Impact of the K-12 School Experience on Gifted Boys
Bibliographic record
Abstract
Gifted learners are frequently marginalized in community classrooms, as they are placed in competition for special education support, with the students who struggle to meet the minimal curricular demands. In this chapter, we describe the practices of identifying and labelling gifted boys, from the perspective of gifted boys attending high school and from the perspectives of a school system. The case discussed is a large urban public school system, which endeavours to effectively identify gifted students and provide them with learner-centred learning environments. We use Wilber's (2000, 2006) Integral model as a conceptual framework to analyze the findings from an empirical study of gifted boys' school experiences (Alisat, 2013). These findings are also supported by our critical praxis, observing and conversing with gifted young people. The Integral Model is a useful framework for understanding the multiple factors impacting gifted students' daily experiences, engagement and achievement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".