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Record W7071232271

Repatriation, Resurgence, and Reconnection: An Investigation of Collecting and Belonging

2025· other· en· W7071232271 on OpenAlexaboutno aff

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2025
Typeother
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsRepatriationIndigenousIdentity (music)NarrativeBureaucracyField (mathematics)The arts
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.925
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.260
GPT teacher head0.424
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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