Comparative Civil Society and Third Sector Research in Voluntas
Bibliographic record
Abstract
At the 2022 ISTR 15th International Conference “Navigating in Turbulent Times: Perspectives and Contributions from the Third Sector” in Montreal, Canada, founding Voluntas editor Helmut Anheier summarized a continuous key challenge of third sector studies: conducting cross-national, comparative research. This very topic started on the pages of Voluntas from the journal’s inception in 1990. We intend to use this virtual issue of Voluntas to explore the comparative approaches to third sector research that have developed in the journal. Voluntas was and remains a major outlet for considering civil society and the third sector in comparative perspective. However, just as recent as 2021, former Voluntas editors Ruth Simsa and Taco Brandsen ( 2021 ) noted that cross-national research in Voluntas was still “rare” (p. 2), calling on the field to engage in more comparative work.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".