Creating Spaces of Engagement: Exploring High School Youth’s Voices in Reshaping the Social Justice Curriculum
Bibliographic record
Abstract
The current structure of formal education makes it difficult for teachers and students to hold meaningful conversations to support high-school youth’s meaning-making of critical social-justice issues. This paper presents data on three high-school youth’s knowledge and experiences with social justice issues during the pandemic. Specifically, the paper aims to explore how youth construct knowledge and counter dominant discourses through utilizing informal learning spaces, such as social media platforms, peer and family conversations, as well as personal encounters. In addition, and more importantly, an exploration of how formal education can incorporate social-justice issues into the curriculum is considered. The analysis of these high school youth’s interview conversations presents their diverging needs to learn about social-justice topics in both formal and informal learning contexts. The data also illustrates the power of their voices in a way that could inform future curriculum development. Discussions and implications highlight the possibility of creating such ethical spaces in formal education to engage in social-justice topics.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".