Marianne Nicolson’s Land-Based Knowledgescape Cliff Painting
Bibliographic record
Abstract
Indigenous epistemologies and ontologies are connected to tribal lands, resilience, and claims for sovereignty. These ways of knowing offer an indispensable resource for Indigenous communities in surviving and resisting assimilationist policies. Modes of Indigenous knowledge are not only discussed in academia and practiced in local spaces but are also integrated into artworks that promote public access to First Nations political agendas within settler nation states. Knowledgescapes can be created as conversive artscapes. They can be placed translocally but also re-integrated into First Nations lands. A reworking of the land as a tribally marked space can be traced in artworks that attack bio- and geopolitical manners of settler societies. The land-marker, place-maker, and artscape Cliff Painting (1998) by Marianne Nicolson shall serve as an exemplification of a specific knowledgescape – created for Indigenous audiences to support their claims, and for non-Indigenous audiences to open up a dialogue on colonial issues within a step-by-step decolonizing discourse.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".