The Measure of Meetings: Forums, Deliberation, and Cultural Policy
Bibliographic record
Abstract
Trusts for their foresight in initiating a project in uncharted territory – the policy dimensions of meetings and conferences. We also want to thank project director Alberta Arthurs, who helped shape the research and provided invaluable advice at every stage of the project. Charles Granquist and the Rockefeller Brother Fund co-hosted an important meeting to discuss the findings of this project, and the executive leadership of many of the national arts service associations gave us access to their conferences and helped This research seeks to answer the question: “Do meetings matter for advancing cultural policy? ” The question is approached theoretically and comparatively by examining the broader literature on policy making, as well specific case studies of meetings in other fields, in order to draw lessons and implications for arts and culture; discursively and ethnographically, by attending the annual meetings of arts service associations and recording and interpreting how people at these meetings talked about problems and policy; and empirically, by looking at a sample of conference program books over ten years and coding and analyzing what issues were discussed and who was invited to discuss them. We also studied, in detail, what a random sample of 40 participants say
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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 | 0.030 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".