Bibliographic record
Abstract
The settler-state of Canada continues to reconcile with the genocide of the original Indigenous custodians of the lands on which we operate, alongside the underserving and discriminating against racialized, black, disabled, and LGBTQ2+ peoples all while navigating a climate crisis, the proliferation of equity, diversity, and inclusion (EDI) nomenclature and plans permeates every social and financial sector. EDI initiatives, while called many names throughout history such as social inclusion and affirmative action, experienced a rise in creation in 2020 because of public outcries for the acknowledgement of systemic racial injustice and pressure to address this ongoing form of violence. Canada’s arts and culture sector is not immune from this scrutiny. Having a long history of engaging in social services, the arts and culture sector is often tasked with “fixing” issues when funding is cut to education, health, and community programs, yet arts institutions are not equipped to do this. This paper follows one resident researcher’s journey as they were tasked with developing an arts civic impact framework suggesting equity practices in the arts. The study used a mixed-methods approach, drawing from the walking interview, reverse photo-elicitation, feminist manifestos and research-creation to bring cultural workers across the country together to develop an accessible tool to carefully engage in equity practices within the sector. As a critique and response to flat and prescriptive EDI plans, what was created based on this cross-country collaboration was a non-linear, spiraling framework existing online that arts organizations can make use of and adapt based on their circumstances. Weaving together a historical account of arts administration, Western managerialism, and the EDI in the arts sector, this article responds to the research question: How can access to the arts and culture sector from coast-to-coast-to-coast be more equitable?
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".