Walking with our sisters: healing through storytelling
Bibliographic record
Abstract
This thesis explores the experiences of members of the Walking with Our Sisters \norganizing committee before, during, and after the installation came to Sudbury, Ontario in \nJanuary 2018. The primary research objective was understanding how storytelling allows for a \ncollaborative and holistic approach to the research process. \nThrough the sharing of Indigenous stories and knowledge, this thesis implicates the \nresearcher as a settler-researcher who was privileged with stories of members of the WWOS \norganizing committee’s journey before, during, and after the installation visited Sudbury in \nJanuary 2018. \nAlthough each participant’s story revealed the uniqueness of everyone’s experiences \nworking in the committee, four major themes emerged from the interviews: 1) personal \nconnections to violence against women 2) relationships, self-care & debriefing, 3) arts-based \nmethods as a form of healing and 4) closing the bundle. Presenting the participants’ interviews \nback, through the process of storytelling, revealed the emotional and personal responses to the \nWWOS installation and created a more collaborative research process than traditional Western \napproaches, thus shifting the power in the research. \nThe results of this research will be useful in contributing to decolonial literature and \nunderstanding the importance of practicing self-care when approaching the traumatic subject \nmatter associated with MMIWG.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".