Beading relations: Reimagining, remapping, and remembering through The Lake Nipissing Beading Project
Bibliographic record
Abstract
As members of the Lake Nipissing Beading Project Collective, we reflect on a community-beaded map project that evolved from a partnership between Nipissing University, Nipissing First Nation, and Dokis First Nation, rooted in the traditional lands of the Nbisiing Nishnaabeg on Robinson Huron Treaty (1850) territory. The Lake Nipissing Beading Project (LNBP) emerged in 2020 as a gesture of community care in response to the socio-spatial challenges of the global COVID-19 pandemic. The traveling beaded map installation reimagines, remaps, and remembers Lake Nipissing and surrounding watersheds through a collaborative process rooted in relationality and reciprocity. Critically situating and subsequently “taking back” provincial geospatial imagery, the project presents one attempt to decolonize geographical tools through creative intervention that combines storytelling practices of beading and countermapping. We reflect on the multi-layered histories enlivened through project activities, including those of our community-academic partnerships. We discuss what it means to recognize the beaded map as a living relation and carrier of living stories, re-orienting spatial knowledges toward an intentional ethics of care and community-led stewardship. In doing so the project re-centers Nbisiing Nishnaabeg relationships to, with, and through the lake.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| 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".