Bibliographic record
Abstract
he sociologist Frank Manning, writing in the early 1980s, claimed that "throughout both the industrialized and developing nations, new celebrations are being created and older ones revived on a scale that is surely unmatched in human history."1Manning may have been overstating the case, but there is evidence that festive celebration in Europe and North America has experienced a renaissance.2In the 1980s, festivity seemed poised to gamer the attention of academics;3 but the prominent theories of festivity were little more than theological treatises loosely grounded in empirical observations of actual festivals,4 the complexity and scale of much festivity made their study difficult, and an interest in cultural flows, migration, and globalization tended to result in overlooking festivals because of their strongly local nature.But festivity is once again drawing the academic's eye, and my aim here is to offer some reflections on studying contemporary festivals, and make a pitch for the use of video as an analytical and interpretive tool.Between the fall of 2004 and the fall of 2006,1 made four trips to Wittenberg, Germany, to conduct ethnographic based research on the 1.Frank Manning, The Celebration of Society, (Bolwing Green: Bowling Green University Press, 1989), 4. 2. See Jeremy Boissevain, Revitalizing European Rituals (New York: Routledge, 1992), a collection of case studies on the resurgence of traditional celebrations across Europe.3. See, for example, Victor Turner, ed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".