“YOU CAN’T ENGAGE IN DECOLONIZING WORK ALONE”
Bibliographic record
Abstract
Relationships are central to the processes and outcomes for both decolonizing work in higher education and communities of practice that support faculty learning and professional development. Yet the development of these meaningful learning relationships is in direct tension with the isolation, precariousness, and impermanence of contract teaching faculty who make up more than half the academic labor force in Canadian universities. Our chapter describes a multidisciplinary online Contract Teaching Faculty (CTF) learning community called “Decolonizing and Indigenizing the Classroom.” Our community set out to challenge power relationships in interconnecting systems in higher education to make space for decolonizing work by building, deepening and expanding new and supportive learning relationships between CTF and with our students. As nine participants we look at the value of CTF learning communities by assessing the ongoing impacts of our commitment to transformative learning—our own and our students. We highlight the role CTF learning communities can play in inspiring and sharing pedagogical innovations. We outline the benefits of CTF learning communities for nurturing community and helping overcome the institutional isolation of CTF and our physical isolation exacerbated by the pandemic. We also recognize the challenges and limitations our CTF learning community faced. As precarious workers who are marginalized by this system that exploits and devalues CTF, how can we teach, model, and learn within the colonial systems at work in higher education when we are relatively powerless to affect change?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".