Considering the role of social media: #BlackLivesMatter as a pedagogical intervention to decolonise curriculum
Bibliographic record
Abstract
The COVID-19 pandemic resulted in a drastic transformation to schooling for students throughout the world. During this period, a number of issues arose in our local, national and global communities, including the death of George Floyd and subsequent protests and rallies organised by #BlackLivesMatter. Living through and witnessing many social issues, coupled with the new and enduring pandemic, furthered our understandings of how young people were engaging with these topics without the structures of schools to support them. This article presents the results of a case study where youth aged 15–17 years shared their experiences and understandings about many social justice issues they were observing. The most significant learning around these issues for youth occurred informally through social media as opposed to in the classroom, reinforcing that schools are not ethical spaces from which to challenge institutional, structural and systemic barriers to justice. As such, this article discusses the potential for formal education to be transformed into an ethical and decolonising space to learn about and challenge injustice.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".