Marginalized Urban Indigenous Youth and the Virtual World of Second Life: Understanding the Past and Building a Hopeful Future
Bibliographic record
Abstract
A small independent high school in the Canadian West is using the affordances of the virtual world of Second Life to explore and reconstruct the colonial past of their students: marginalized urban Indigenous youth. The affordances of the virtual world make it possible to reconstruct the past, deconstruct the present and create a possible hope-filled future. This process is underpinned by pedagogies of engagement and emancipation on three virtual islands (sims) in the virtual world. The past was reconstructed and can be deconstructed on the Negan Tapeh sim. Negan Tapeh is a Cree phrase meaning “look to the future.” When the activities and quests associated with exploring and understanding the events of the past and their impact on the present are complete, participants are transported to the virtual present on the Boyle Street sim.Boyle Street is an inner-city area in Edmonton, Alberta, where most of the youth live or gravitate to. In Canada and the United States, inner city areas have historically been synonymous with depressed and run-down parts of the city where petty crime, violence and substance abuse are woven into the fabric of daily life. On Boyle Street, the youth are tasked with completing assignments (quests or hunts) distributed by teachers (scripted agents) in the sim’s Boyle Street High School. When the three quests are completed, participants receive their key to the future. The future unfolds on the third sim Urban Hope.This paper underlines the importance of the virtual world in educational projects with marginalized youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".