“I Never Read Anything Like That Before:” Mapping the Identities of Blackfoot Readers
Bibliographic record
Abstract
Our research emerges out of a concern that Indigenous readers, generally speaking, are not having opportunities to read and discuss culturally relevant fiction. Children’s literature and reader response scholarship does not fully engage with what Indigenous voices could bring to our understanding of young people's responses to and engagement with fiction. We are currently conducting a community-based, participatory project with Blackfoot First Nations young adults who live on the Kainai Blood Reserve in southern Alberta. We are looking at the ways in which our participants perceive of and represent their social, cultural and place-based identities within and beyond the text. Our participants are reading and discussing several Indigenous texts, including a graphic novel set on their reserve. We are interested in the ways in which these readers reflect on their identities while discussing culturally relevant fiction, within reading discussion groups and the creation of journals (comprised of visual responses, such as maps, sketches, and photos). Within this article, we share how using culturally relevant and local, place-based fiction is spurring Blackfoot youth to have discussions about their identities within and beyond the text. We suggest that these methodological approaches are empowering the Blackfoot youth to develop their own self-representations by relating these stories to their own lives, including their memories of growing up on a reserve. In positioning our participants as experts in their own cultures and lived experiences, they are visualizing their own diversity, complexity and importance in the world.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".