Sustainable Development and Cultural Policy: Do They Make A Happy Marriage?
Bibliographic record
Abstract
The aim of this article is to foster debate within the academic community on the notion of culture as the fourth pillar of sustainable development. Is Agenda 21 for Culture not just another way of making the case for increased funding of the arts by the different governments? Are the goals of this fourth pillar not the same as those traditionally found in cultural policies? This article looks at the origins of Agenda 21 and raises questions about its relationship with the challenges facing cultural and arts organizations, the different definitions of the term “culture,” and the distinction between high and popular culture. It explores the links between these questions and the economic and market issues confronting stakeholders in the cultural sector as well as public policy-makers.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.055 |
| Scholarly communication | 0.031 | 0.054 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 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".