Indigenous littoral curation: a viable framework for collaborative and dialogic curatorial practice
Bibliographic record
Abstract
This dissertation explores what I currently term “Indigenous Littoral Curation”. Littoral artists and scholars acknowledge littoral sites, the shorelines where the water meets the land, as a metaphor for dialogical and socially engaged artistic strategies that create meaningful change. This is applicable in naming how certain First Nations, Métis, and Inuit curators strive to contribute to Indigenous communities and nations by centering collaborative process and dialogue. These processes mirror Indigenous research methodologies, which are grounded in Indigenous Knowledge Systems and lived experiences, and are reminiscent of littoral art practice and paracuratorial practices. In this dissertation I create space to contemplate, critique and name the actions of curators who prioritize Indigenous Knowledge Systems, open-ended dialogue and collaboration when working with artists, communities, and art organizations. To do so, I consider scholarship about Indigenous Knowledge Systems and research methodologies, and how they apply or contribute to curatorial practice. This involves engaging in dialogues with other curators and scholars, and centering the sharing of personal narratives and first-hand accounts of their practices. Teachings provided by Michif knowledge keeper and language carrier, Verna DeMontigny infuses this dissertation with Michif language and land-based knowledge systems that advocate affinity with the natural and human worlds. Organized formal and informal kitchen table talk gatherings and beading sessions have created sites for open-ended dialogues and self-reflection, which led to naming and igniting curatorial strategies to help keep or bring Indigenous hearts home. As well, this allowed for a reflective inquiry into my specific kinship and community ties, and the ways they impact and direct my curatorial practice. In addition to relying on Indigenous Knowledge Systems and methodologies, I look to art and curatorial studies advocating reciprocity, relationality, self-critique and interrogation. This approach includes a consideration of littoral art and paracuratorial practices which prioritize holding participants socially accountable through collaborations between artists, various communities and agencies. I investigate, write and employ from a Michif paradigm, which requires placing myself along the washagay, or shoreline. I believe it is here we are able to hear our ancestors whisper in our ears, mobilize the present, and dream the future.
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 | 0.042 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.105 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".