Decolonizing Practices — With Ta7talíya Michelle Nahanee
Bibliographic record
Abstract
Ta7talíya Nahanee joins Am Johal to discuss her work in creating social change through decolonial facilitation, rooted in Indigenous ways of knowing and chenchenstway, the law of lifting each other up. Ta7talíya shares her journey of founding Decolonizing Practices, developing Sínulhkay and Ladders, and how she engages people in conversations about redress, land equity, privilege, and resisting the comfort of complacency in neocolonial systems. She also speaks to Indigenous language resurgence, decolonizing identity, and the idea that decolonization is an ongoing and personal process of self-actualization.\nTa7talíya Michelle Nahanee, Sḵwx̱wú7mesh, is a decolonial facilitator & strategist catalyzing social change to transform colonial narratives & impacts. She works within the intersection of class, culture and creativity focusing on social change through communications and engagement. Ta7talíya’s collaborations have influenced opinions, changed behaviours and mobilized community action. She is the designer of a life-size board game called Sínulhkay and Ladders which she uses in her workshop Decolonizing Practices. Her approach earned her the 2019 City of Vancouver Award of Excellence in Diversity and Inclusion. Ta7talíya is also a 2020 Dialogue Associate with the Simon Fraser University Morris J. Wosk Centre for Dialogue. She is the author and designer of a decolonizing workbook called Decolonize First, a liberating guide and workbook for peeling back the layers of neocolonialism. Ta7talíya also recently co-founded a Squamish-led non-profit called M̓i tel'nexw Leadership Society to work with organizations and individuals who want to activate decolonizing practices, indigenization and commitments to reconciliation led by Squamish ways of seeing, knowing & being.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".