Cross-Cultural Connections: An Investigation of Singing Canadian and American Patriotic Songs
Bibliographic record
Abstract
Abstract The purpose of this study is to compare American and Canadian high school choral students’ knowledge of their respective patriotic songs. The questions of the study are as follows: (a) do students sing accurately their respective national anthems relative to melody and lyrics; (b) do students sing accurately "America" and "God Save the Queen" relative to melody and lyrics; (c) do students sing accurately the national anthems of each other’s country relative to melody and lyrics; and (d) is there a difference in the accuracy when students sing their respective and each other’s patriotic songs? The sample consisted of 102 secondary school students who were enrolled in non-auditioned choir classes and audio taped singing unaccompanied versions of their respective national anthems and "America" or "God Save the Queen." Results indicated that overall, Americans were significantly more proficient than Canadian singers. When converted to percentages, 77% of American students and 41% of Canadian students were judged as proficient when singing lyrics and melody of their own National Anthem. American students were significantly more accurate (p < .0001) in melody and lyrics when singing "America" than Canadian students who performed "God Save the Queen." Implications for practical application indicate that more emphasis should be placed on giving choir students the opportunity to sing their own national anthems, with special attention to typical lyric mistakes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".