Cultural participation through digital technology : A puzzling issue for cultural governance
Bibliographic record
Abstract
In Québec, digital technology has been perceived in recent years as an opportunity for cultural organizations to renew their relationship with audiences. This vision is promoted by both the government and civil society initiatives as they see in technology potential solutions to challenges related to cultural participation. Based on two focus groups with representatives of the arts and culture sector, this chapter examines the goals, but also the recurring issues, cultural organizations encounter when implementing technological solutions. Cultural organizations generally seek to optimize the experience of actual audiences and reach out to new audiences through technological means. However, many of these projects involve collecting and analysing data about audiences, which requires new skills and additional resources. This reality on the ground is then confronted to the main policy put in place by the government to support the digital turn in the sector. This policy raises concerns among arts and culture professionals regarding their capacity to meet new expectations set out by the government. If some professionals are enthusiastic about the new possibilities offered by technology, others feel they are always in catch-up mode, whereas a last group is rather resisting the movement toward more technology in the sector.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".