Learning Discomfort and Uncertainty: The KAIROS Blanket Exercise as a Canadian Settler Education Tool
Bibliographic record
Abstract
The KAIROS Blanket Exercise is an experiential learning activity that takes participants in\nCanada through Indigenous history in North America from an Indigenous perspective. In a 90-\nminute workshop, participants embody the role of Indigenous peoples and walk on blankets\nthat represent the land. Through the reading of scripts, they re-enact the chronology of Canadian\nhistory and the processes of settler colonization and then debrief together to discuss their\nexperiences in the exercise. The popularity and wide-spread use of the Blanket Exercise since\nthe release of the Truth and Reconciliation Report in 2015 as a settler teaching tool illustrates\nthe need to study its educative impact and aims. The premise of this thesis is that settler\neducation is a needed area of focus for transforming the settler-Indigenous relationship into one\nthat is less colonial and less attached to a settled Canadian future. This thesis uses the Blanket\nExercise as a case study to reveal settler Canadian investments in settler futurity and examine\npotentials for disrupting those investments. This study considers that discomfort and emotions\nare a critical aspect to this education and uses Boler’s Pedagogy of Discomfort and Ahmed’s\nCultural Politics of Emotions as theoretical frameworks to unpack settler reactions and\nresistances in the Blanket Exercise. This thesis uses Grounded Theory qualitative methods to\npresent interviews with KAIROS staff and KAIROS blog posts as sources of data analysis in\norder to study the potential space the exercise creates for unlearning in settler Participants.\nThe findings of this thesis reveal that though the Blanket Exercise does have the\npotential to create space for unlearning in settler Participants, this potential is not always\nreached in the immediate space of the exercise. This is due to the introductory nature of the\nexercise and Participants’ engagement at easier shifts in learning. However, the study considers\nthat Participants in the exercise are experiencing a learned moment of discomfort that can be\ncultivated in settlers beyond the timeframe of the exercise to reduce the harm that these\npractices of futurity have on settler-Indigenous relationships in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".