Bibliographic record
Abstract
In taking up the challenge of writing this report I brought to the process field work research done in 1999 for Soci 602. This research involved a women's performance group made up of a chorus of ninety plus women and several quartets. The Christchurch City Chorus of Sweet Adelines International are a cappella singers who harmonize in the barbershop style. They are part of a wider New Zealand organisation and at the same time are also part of the international organisation which has its headquarters in the United States of America. My objectives in continuing with this research have been to deepen my appreciation of the theoretical background to my fieldwork and to further my understanding of barbershoppers as performers. I was particularly interested in familarising myself with other studies which had been done in this area such as Robert A Stebbins' book The Barbershop Singer: inside the musical world of a musical hobby. I also needed to explore the concept of ""serious leisure"" as it has been developed by Stebbins with a particular view to understanding how the concept has been applied to this art form. Another further objective was to explore the concept of ""mateship"" as it has been used in New Zealand. In my original paper for Soci 602 I proposed that this concept may make sense of the exclusivity of the social aspects of the women's organisation. The Summer Scholarship gave me an opportunity to follow-up this conjecture.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".