Bibliographic record
Abstract
History has not been kind to Indigenous women. Like a fungus, colonialism attacks the roots of their sovereignty and leadership. From the formation of damaging policies to public demonizing, Indigenous women’s bodies have been attacked, rendered embarrassing, labeled as wrong, or emptied of identity. In the face of current and ongoing legacies of colonization, Indigenous women’s voices are resurfacing and regaining power. Resilient Indigenous scholars, writers, artists, and community members are changing narratives and inspiring a resurgence of Indigenous activism, often with Indigenous women and two-spirit peoples at the fore. This chapter uses a case study to consider performance-based practices, the role of traditional knowledge in curation, as well as Indigenous theories of refusal, recognition, and resurgence. In October 2017, the Plug In Institute of Contemporary Art in Winnipeg, Manitoba, held a large exhibition titled Entering the Landscape , with 21 participating artists, curated by Jenifer Papararo and Sarah Nesbitt. Through photographic, performative, and installation-based works, Entering the Landscape focused on relationships between the body and the land. Stemming from this exhibition, this chapter unfolds as a way to think through the unbodying of Indigenous women and the possibilities for acts of rebodying in museum and gallery spaces.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".