Bibliographic record
Abstract
THE TOPIC OF LANGUAGE and music has interested scholars of various research fields. Some scholars in musicological literature strive to apply linguistic theory to musical analysis (e.g., Feld 1974; Feld and Fox 1994; Netti 1958), and cognitive studies show how similarly the human brain processes music and language (Patel 1998, 2003). This interdisciplinary subject, however, has not received significant attention in mainstream linguistics. A few linguists, including Hayes and MacEachern (1996, 1998), Hinton (1984), and Fitzgerald (1998), provide linguistic analysis of folksongs or indigenous songs, but a common aspect of linguistic analyses of folk verses in general is that they are based on songs that are well known, if not already documented. Indigenous songs are not only rarely documented, but they are also documented mostly by ethnomusicologists. Because of this, the music itself is well recognized, but recording and analysis of lyrics tend to be understudied. When lyrics and their linguistically important information such as morphosyntax and phonology are documented, the opportunities for folksong study can be significantly expanded. This paper provides preliminary analysis of five Blackfoot lullabies as the first step toward a full account of Blackfoot lullabies. The organization of the paper is as follows. In section 1, 1 briefly outline the background of the Blackfoot language, choice of song type, Blackfoot song collections, Native American folksong study, and the fieldwork process. In section 2, 1 describe lyrics, utilizing English translation. In section 3, a brief discussion of the characteristics of the songs is provided. In section 4, I discuss the relationship between the metrics of language and songs.1 BACKGROUND1.1 LanguageBlackfoot is an Algonquian language spoken in Alberta, Canada, and northwestern Montana, United States. There is one tribal group in the United States, Aamsskapipikani (Southern Piegan), and there are three in Canada: Siksika (Blackfoot), Kainai (Blood), and Aapatohsipikani (Northern Piegan). There are dialectal variations among these groups (Frantz and Russell 1995). The present study is based on the dialect spoken by the Southern Piegan tribe, which resides on the Blackfeet Reservation in Montana.2 As is the case with other indigenous languages, the number of native Blackfoot speakers drastically decreases every year. The United States Census of 2000 shows that the population of Blackfoot speakers in Glacier County, Montana, is approximately 1,450 people. According to a survey conducted by the Piegan Institute, however, the situation is more dire. They estimated the number of proficient speakers at 100, and these speakers are seventy-five years old or older (Darreil Kipp p.c.).3 In addition, the Piegan Institute's study found that the proficiency of self-claiming Blackfoot speakers varies significantly. As a clear indication of such language decline, Blackfoot lullabies are no longer sung to infants. I hope that my work here will be used not only by linguists and ethnomusicologists, but also by teachers and parents of the Blackfeet Reservation to enhance native knowledge of Blackfoot language and culture.1.2 Choice of Song TypeMost American Indian songs I came across in various forms, such as recordings of powwow songs, had abundant vocables with few if any meaningful words. Vocables are nonsense words sung along with the melodies. These powwow songs are exchanged and sung by members of different tribes regardless of the similarity in linguistic traditions between the tribes. However, linguistic study is best conducted on songs that have lyrics with recognizable words; for this reason, I looked for songs that were not subject to cross-tribal exchanges. If songs are not exchanged cross-tribally and stay within the same social-linguistic group, lyrics can keep their original, meaningful forms. Such songs may be those that are sung to children. Among children's songs, lullabies are sung to infants being raised as members of the society, and they are not likely to be shared beyond a linguistic community. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".