Bibliographic record
Abstract
Time to Move Webwards?Nobody has yet written a general survey of Canadian traditional music equivalent to A. L. Lloyd's Folk Song in England.It is a great shame that Edith Fowke, who certainly had the knowledge and the ability, never got around to doing so.Yet anyone who has tried to study, let alone teach, Canadian folksong in a systematic way knows that the lack of a good textbook is but one of many obstacles.Frankly, the current state of affairs is distressing.Resources are scattered and difficult to access.Primary resources -manuscripts and recordingsare contained in archives and libraries ranging from Victoria Municipal Library on the West Coast to the MUN Folklore Archives in St. John's, Newfoundland.Most printed primary sources (song collections) are out of print, so that libraries cannot purchase them and they remain inaccessible to undergraduates except at a few privileged locations.The same is true of many secondary sources, which in any case are not that numerous.An obvious way to begin to remedy this lamentable situation would be to create a web site where researchers, teachers and students could access otherwise unavailable sources.The aim of this discussion paper is to examine some of the steps required to create such an online resource.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.367 | 0.249 |
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".