BORN TO SING: FIJI'S "SINGING CULTURE" AND IMPLICATIONS FOR MUSIC EDUCATION IN CANADA
Bibliographic record
Abstract
ABSTRACT. The first part of trus paper describes the singing practices of selected Fijian communities, and identifies some of the social conditions that support widespread and skillful singing. Next, in order to interpret Fijian musical practices, it proposes a cultural grammar - a set of guidelines or rules that define what individuals within a society, community, or group have to know, produce, predict, interpret or evaluate within a given setting or social group in order to participate appropriately (Heath, 1982; Heath, 1983). In the second part of the paper the proposed cultural grammar is used to generate suggestions for developing a singing culture in a Canadian school. NE POUR CHANTER: LA CULTURE DU CHANT A FIDJI ET LES IMPLICATIONS POUR L'ENSEIGNEMENT DE LA MUSIQUE AU CANADA RESUME. La premiere partie de ce rapport decrit la pratique du chant de certaines communautes fidjiennes tout en mettant en evidence quelques unes des conditions sociales contribuant a repandre l'art du chant. Pour interpreter les pratiques musicales fidjiennes, l'auteur propose une grammaire culturelle, a savoir un ensemble de directives ou de regles definissant ce que les sujets d'une societe, d'une collectivite ou d'un groupe doivent connaitre, produire, prevoir, interpreter ou evaluer dans un milieu ou un groupe social donne pour y participer pleinement. (Heath, 1982; Heath, 1983). Dans la deuxieme partie de ce rapport, la grammaire culturelle proposee sert a emettre des conseils en vue de l'elaboration d'une culture du chant dans une ecole canadienne.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".