Transformation Towards Social Justice: Artistic Amalgams Seen Through a Hip-Hop Pedagogical Lens
Bibliographic record
Abstract
There is beauty in evolution, a need for us as humans to understand the essence of reform and how it begins.As educators, we seek to explore how students in the United States understand social justice.Classrooms can be an agent of change -a venue where teachers can encourage and nurture new ideas.We were interested in exploring this relationship and sought to explore how our higher education classrooms were an agent of change, a first step in an ongoing societal conversation about social justice as experienced by students in an undergraduate honors course, "Hip-Hop and Social Justice."In this creative essay, we explore the nature of transformation, specifically how students engaged in injustice when presented through hip-hop culture.We examined explicit (what students said) and implicit (how students presented) responses as students worked to understand the nature of transformation from the student perspective, and how it was related to social justice.Transformation becomes necessary when human rights are out of balance.This theme -transformation -is a central one touched on within the course and widely represented through the hip-hop genre.Transformation can be defined as a conversion, a change that happens as variables adjust and reconfigure.Piaget's notions of accommodation and assimilation come to mind -as individuals encounter new information we go through a cognitive process whereby we examine new information and process it.Familiar information and experiences are easily assimilated and fit or enhance our preexisting schema.However, novel concepts or understandings often have to be examined more deeply, leading us to the accommodation process whereby old ideas are
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.018 | 0.005 |
| 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".