Bibliographic record
Abstract
In this last year there have been many indications that the Folklore discipline is gaining considerably in both academic and general recognition in this country, much as it is abroad. Specifically, not only have there been our own Association's members contributing to major folklore and related conferences in North America and the United Kingdom, but Graham Seal was invited to give a keynote opening address - on concepts concerned with ANZAC - to a special inaugural Folklore Conference at the Victoria University of Wellington, in the New Zealand capital. Like grand theme conferences continue to be associated with the National Library in Canberra, while more applied ones are linked with state libraries and regional festivals. Similarly, it is pleasing that a long 2005-written paper from an AFA member has more recently appeared in the electronic journal, 'Folklore' edited in Estonia. It may also be noted that Australian scholars represented in this present issue reach out to field materials in Burma, Canada, and elsewhere, while many of their themes are 'global contemporary'. We have also been interested in the way in which Folklore and Ethnography, perhaps, rather than Anthropology, may be said to be coming together. Of course, this has been the case in some sense, for many years, and the matter has been discussed in our pages.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.124 | 0.078 |
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".