A Place for Us to Sit: Making and Remembering Visual and Material Histories of Deshkan Ziibiing
Bibliographic record
Abstract
A Place for Us to Sit: Making and Remembering Visual and Material Histories of Deshkan Ziibiing tracks the journey that I undertook to locate ancestral belongings and to learn the stories and practices of making that are unique to the Anishinaabeg of Deshkan Ziibiing (Chippewas of the Thames First Nation)¬¬¬¬a First Nation community located in southwestern Ontario, Canada. Through visits to museum collections and archives, this dissertation traces a journey of reconnecting with Deshkan Ziibiing material culture, which I refer to as gete-anishinaabeg, the old ones. This process is further enriched by oral histories that identify how colonialism has impacted Deshkan Ziibiing makers and how the community is and has been reclaiming practices of making. \nThis dissertation was inspired by a collection of gete-anishinaabeg that now resides at the American Museum of Natural History. The collection, acquired by American Anthropologist Mark Raymond Harrington in 1907, raises critical questions for me as someone who has grown up not knowing, seeing, and engaging with the arts of my ancestors—furthering the desire to learn about what was happening in the lives of Deshkan Ziibiing families before and after Harrington’s collecting expedition. Through the recovery and re-engagement with the things our ancestors left behind, I resituate gete-anishinaabeg in a continuum of making practices that embody Anishinaabe futurity. Grounded in Indigenous methodologies such as Biskaabiiyang, Storywork, Felt Theory, and Native Feminist Analysis this dissertation reveals complex layers of interconnected histories and adds to the emerging discourse of art histories that are specific to Indigenous communities and place. This work builds upon decolonizing praxis as theorized by Michelle M. Jacob (Yakama) and fosters a process of reconnecting Indigenous minds and bodies to the things our ancestors made, the experiences they felt, and the knowledge they created.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".