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Record W2319377949 · doi:10.1177/0255761411433722

Patriotism, nationalism, and national identity in music education: ‘O Canada,’ how well do we know thee?

2012· article· en· W2319377949 on OpenAlexaffabout
Susan C. Guerrini

Bibliographic record

VenueInternational Journal of Music Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAnthemSingingLyricsChoirPatriotismMusic educationPsychologyPedagogyHistoryArtLiteraturePolitical science

Abstract

fetched live from OpenAlex

The purpose of the study was to determine Canadian secondary school choral students’ skill in singing the national anthem. The sample ( N = 275) consisted of students from 12 schools, representing six provinces in Canada. Students were audio taped singing ‘O Canada’ in English, French, or in a combination of both languages and subsequently completed a questionnaire. Results indicated that few students could sing the national anthem perfectly. Although students were significantly more accurate in remembering the lyrics than in singing the melody ( p < .0001), only 67% were judged proficient in lyrics whereas a mere 46% were judged proficient in melody. Possible reasons for these poor results include the frequency with which students sing the anthem in secondary schools, the fact that three-quarters named a classroom teacher in the early/elementary years as being the one responsible for teaching them the anthem, the shift to solo versus group singing in public events, and the inconsistency with which music education is delivered in elementary schools. Implications for practice indicate that more emphasis be placed on assisting choir members to sing the anthem accurately, more opportunities be provided in secondary schools for students to sing the anthem, and more curricular attention be placed on teaching students both English and French versions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.066
GPT teacher head0.452
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2012
Admission routes2
Has abstractyes

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