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Record W2207451240

Storytelling with cultural tools: children’s engagement with features of oral traditions in First Nations cultural education programs

2013· dissertation· en· W2207451240 on OpenAlexaboutno aff
James Allen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingPedagogyPolitical scienceSociologyNarrativeLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

This dissertation presents a comparative case-study of how two groups of culturally diverse elementary school students engage with particular forms of narrative practice shared by cultural educators through First Nations cultural education programs. The project develops the argument that different cultures afford different symbolic resources useful in “structuring” and “organizing” experience for individuals and that one important way in which these “possible worlds” are shared in a community is through storytelling. To develop this argument the project was structured around two main research questions: 1) what are the forms and functions of narrative practices that children experience during the First Nations cultural education programs? And 2) how do children “echo” and “transform” these narrative practices through their participation in the narrative activities organized around the programs? Participants in the project were two First Nations cultural educators conducting cultural education programs in public schools who participated as research partners, as well as 16 students from a grade 1 classroom (Class A) who participated in the first educator’s program and 15 students from a grade 4 classroom (Class B) who participated in the second educator’s program. Data for this project came from a multiple sources and analysis focused especially on stories told from the cultural educators during their programs as well as retellings of these stories from students in the two classrooms. Additional data was included from interviews and discussions with the cultural educators and student participants, field notes on the cultural education programs, and the classroom communities, as well as discussions with classroom teachers. This additional data was integrated into the project at various points to support interpretations. An ethnopoetic or verse analysis (Hymes, 1981, 1996, 2003) of stories told by the cultural educators revealed recurring patterns in the stories that both educators employed for particular rhetorical effects. In addition, these patterns revealed a number of “cultural features” of the storytelling performances that the educators used to emphasize specific points, to make parts of the stories especially memorable for the audience and to share lessons with the audience. Verse analyses of students’ story-retellings revealed a number of ways in which these students echoed and transformed these cultural features and made use of them to share the meaning or lesson of the stories. Finally, comparative analyses of story-retellings from the differently aged students in the two classrooms through a number of analytical frameworks showed that the retellings from grade 4 students were more complex in a number of ways, but also that students in both classrooms skillfully employed these different forms of narrative resources. The results reported in this study suggest that students were making use of the space provided in the cultural education programs to explore particular forms of narrative practice shared by the cultural educators and that they were making use of these narrative resources in meaningful ways.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.314
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2013
Admission routes1
Has abstractno

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