Repatriation, Resurgence, and Reconnection: An Investigation of Collecting and Belonging
Bibliographic record
Abstract
This thesis focuses on repatriation, resurgence, and reconnection. In line with Indigenous methodologies of prologuing and story, I employ the auto-ethnographic method to detail my experience of Indigenous identity and investigating my relationship to my father’s Métis heritage. I further engage in the practice of story through qualitative, semi-structured, conversational interviews with two Indigenous arts professionals currently working in the field of repatriation. Through narrative analysis and a critical museological framework, these conversations form the foundations for an exploration into strategies of repatriation and the role stolen cultural belongings play in the collections of galleries, museums, and other heritage organizations. What emerges from the conjunction of these stories is a treatise on collecting and belonging through the lens of personal and professional functions of return. This research seeks to demystify the practice of repatriation as a one-time, almost bureaucratic occurrence and instead posits it as one tool of relationship building between museums and communities. Similarly, I show how the processes of reconnection and resurgence also struggle to be linear events. Through this combination of auto-ethnography and conversational interviews, I explore how access to or absence of emotional and material expressions of self shape identity through a critique of what collecting practices we engage in as both individuals and institutions.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".