Bibliographic record
Abstract
Abstract So near, so far”-or is it rather “Too near, too far”: the polar situations of would-be jazz musicians in Canada and Australia. In considering their situations we immediately face the question of individual voice versus local dialect. Can we speak of shared musical attributes that could define a Canadian or Australian jazz sound? And how can the individual voices that will generate the dialect form in the first place? Canadian jazz historian Mark Miller poses the “so near” syndrome. Not only is the north-of-border jazzman likely to be well informed of the latest developments to the south by the availability of recordings and by the relative ease of experiencing the U.S. jazz scene in the flesh. Canada is also only a slight northerly swing in any tour of the States. A successful Canadian musician is likely to be subsumed readily into the American or international jazz network, ceasing to be generally recognized as Canadian; witness the notable examples of composer-arranger Gil Evans, pianists Oscar Peterson and Paul Bley, and trumpeters Maynard Ferguson and Kenny Wheeler. That the emigration of future jazzmen of note from Canada began early is illustrated by the example of pianist Tiny Parham, who recorded in Chicago in the 1920s.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".