Understanding Educational Experiences of Belonging for Incarcerated Males
Bibliographic record
Abstract
Having worked with in-risk teens for the past 17 years, along with researching incarcerated male teens, it is apparent that many dysregulated youths are leaving school early and getting involved in deviant behaviours. Rather than looking for ways to connect with in-risk youth, many educational professionals respond with judgement and consequences in the form of punishment. It is imperative that we learn about the lived experiences of our in-risk teens, and in turn understand why they are dysregulated. I have come to understand that to reach in-risk youth we need to find ways to create and build connection and understanding. This podcast hopes to share the connection between in-risk youth's educational experiences and the potential of getting involved in criminal activity, along with sharing ways that educators can shift their practice with the intent of connecting with their students in a trauma informed manner. As we connect with our youth, and they begin to feel a greater sense of belonging, we can in turn impact their academic engagement, community involvement, and potentially reduce the risk of pushing students out of the educational system early and into criminal activity. This podcast addresses areas of equity, student success, retention, and recidivism
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".