The Virtual Platform for Indigenous Art: An Indigenous-led Digital Strategy
Bibliographic record
Abstract
Over the past decade, a multitude of digital platforms engaging with Indigenous collections of ancestral belongings have been developed for the public in an effort to reconsider and reconceptualize notions of access and Indigenous ownership in virtual space. An initiative in partnership with the Onsite Gallery, the Virtual Platform for Indigenous Art (VPIA) is a newly developed resource that originates from Dr. Gerald McMaster’s Entangled Gaze Project at the Wapatah Centre for Indigenous Visual Knowledge, OCAD University in Toronto, Canada. VPIA is a strategic digital platform that brings together a specific dataset of Indigenous artworks and cultural belongings that portray European and Asian newcomers to Turtle Island, drawn from global museum collections. The platform’s innovative approach to collections is grounded in a dual record format, where visitors are invited to create a Community-Member profile and contribute knowledge and information to artwork pages that consist of a permanent institutional record and an evolving community-generated VPIA record. The VPIA is intended to bridge communities and institutions to facilitate digital contributions of novel ideas about the Turtle Island contact zone and the implications of the colonial period, from early contact through to the twentieth century.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".