Participation Studies and Cross-National Comparison: Proliferation, Prudence, and Possibility
Bibliographic record
Abstract
Over the last three decades the practice of surveying a country's population to gauge participation in various arts and cultural activities has spread. This paper considers twenty different contemporary participation studies, which cover thirty-five countries (thirty-six if you distinguish the study for the United Kingdom from the study for England) plus the Canadian province of Québec. The paper is restricted to what might be described as “traditional” participation studies—random surveys of the adult population to ascertain the participation of various demographic groups in one or another cultural behavior in the previous twelve months. Typically, the results of these surveys are summarized in a participation rate—the percentage of the demographic group that has reported a particular form of participation. Collectively, the studies summarized here provide forty-five different sets of participation rates. That so many participation studies now exist for so many countries invites comparison. But what sort of comparison is possible? The paper begins with a consideration of the various definitions of “participation” and looks at the history of participation studies. I then address the issue of comparability, particularly with respect to variation in the design of participation studies. I also address the issue of facilitating the interpretation and use of participation data, which inevitably leads to the question of the extent to which the results of participation studies actually impact policy choices. Comparable data are not necessarily usable data, but neither are usable data necessarily comparable data. Differences in methodology, it turns out, severely restrict one's ability to compare responsibly. Nevertheless, it does seem possible to articulate some broad hypotheses across countries. Still, the primary conclusion is that while one should be wary of ex post harmonization of participation studies, one should also be wary of ex ante harmonization. What has been created, in the end, is a research terrain in which (cross-national) comparability is traded off against (local) usability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".