Bibliographic record
Abstract
Over the last two years of the pandemic, the arts, culture and heritage sectors have experienced unique challenges. These sectors have been amongst those hit hardest and longest by the COVID-19 pandemic. This has not only affected their economic viability but also their relationship with the public, whose interests, expectations, access and behaviours have shifted dramatically over the last two years. The pandemic accelerated a shift already underway in Canadian society. Polarization, disinformation, an unequal set of rules for the foreign online broadcasters and social media companies challenge social cohesion in Canada. This reality, combined with growing awareness of systemic racism and a greater acceptance of the need to confront the climate crisis have created a need to reimagine policies, principles, and practices to ensure continued long-term, sustainable growth, competitiveness, and vitality of the arts, culture, and heritage sectors in Canada. Canada’s arts, culture and heritage sectors play a fundamental role in building a stronger, more cohesive and resilient society. These sectors are not only significant economic drivers but they also contribute to Canada’s identity and social fabric. They help Canadians create meaning and forge a stronger shared identity based on shared values. However, to achieve these beneficial effects, there needs to be a renewed and shared understanding of these sectors’ fundamental roles and value. The Government of Canada convened the National Culture Summit: The Future of Arts, Culture and Heritage in Canada to help these sectors recover and thrive as well as mobilized these sectors around positive economic and social outcomes for Canada and discussed ways to support their own recovery and long-term growth and competitiveness. The Summit brought together leaders from across the country from these sectors for a national conversation on resilience, sustainability and transformation of the arts, culture and heritage sectors in Canada. The summit’s four themes were: •Promoting long-term competitiveness and growth; •The return of visitors and engaging new audiences; •The role of digital platforms in arts, culture and heritage sectors; and •The contribution of cultural sectors to reconciliation, combatting climate change and building an open and more inclusive society.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.098 | 0.012 |
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".