Unsettling allyship, unlearning and learning towards decolonising solidarity
Bibliographic record
Abstract
Social movements are pedagogical spaces for collective learning across difference. Divergent worldviews, interest and identity, historical legacies and relations of power complicate notions of allyship and solidarity for common cause. In this article, we draw on social movement and transformative learning to reflect on our experiences of learning and unlearning as white settler-colonialists researching allyship in the Standing Rock struggle against an oil pipeline in the United States. Our text is also shaped by our own experience as activists within a local Indigenous-led movement to protect ocean and land from the Trudeau–Kinder Morgan oil pipeline. First we introduce ourselves, our research project, context and argument. We then position our work within social movement and transformative learning scholarship, critique notions of allyship and then solidarity. We argue for the unlearning of colonial practices and mindsets which centre our particular white colonial knowledge, leadership, privilege, power and bodies and learning towards decolonising solidarity. To illustrate this process, we present three personal vignettes that speak about the start of our own 'unlearning of ourselves', and learning of decolonising solidarity. We conclude the article with a discussion of how best we believe learning towards decolonising solidarity might proceed in social movements.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".