Dreaming in Indian: Contemporary Native American Voices, ed. by L. Charleyboy & M. Leatherdale
Bibliographic record
Abstract
Charleyboy, Lisa, and Mary Leatherdale, editors. Dreaming in Indian: Contemporary Native American Voices. Annick Press, 2016.This magazine-like anthology for young adults presents a plurality of contemporary Native American voices, using beautiful design and high quality photographic layouts. The forms of expression these voices take are varied, and include poetry, art, memoir, hip-hop lyrics, question and answer interviews, fiction pieces, and fashion photography, among others. Over fifty pieces are featured, from well-known Native American artists from across North America, such as throat singer Tanya Tagaq Gillis, author Joseph Boyden, and actress Michelle Thrush, although many other voices are represented too, including those of chefs, youths, fashion designers, journalists, and more. Native American Nations from all over North America are represented, including Blackfoot, Cree, Blood, Metis, and mixed race. Short biographies on each contributor are featured at the end of the book. Although Dreaming in Indian is meant for young adults, it will appeal to a broad spectrum of the population, including children and adults.Dreaming in Indian is presented in four different sections: ‘Roots,’ ‘Battles,’ ‘Medicines,’ and ‘Dreamcatchers’. ‘Roots’ covers pieces related to ideas of home and the past, ‘Battles’ examines issues such as racism, gender identity, abuse, addiction, and poverty, ‘Medicines’ shows the innovative ways youth have found healing in music, art, sports, and cultural traditions such as jingle dancing and hoop dancing, and ‘Dreamcatcher’ looks towards how Native people are currently shaping the future for Native youth. Topics covered within these sections also include bullying, the effects of residential schools, and suicide, but also extend to feeling like an outsider both within and outside of your culture, career advice, how culture must grow, and reactions to stereotypical portrayals of Native Americans in popular culture. In addressing these issues, Dreaming in Indian offers glimpses and directions for how to move forward without patronizing or becoming removed from the topic. Even more powerful are the multiple viewpoints that are brought to the same issue. Different Native voices often offer differing thoughts on the same topic, illustrating how myriad and complex the people and these issues are.Ultimately, Dreaming in Indian is a powerful book that provides a well realized portrayal of Native people by Native people. As a non-fiction work, it fills a niche not often addressed, and showcases the talent and passion of the people it engages. The book can be read in a couple of hours, but due to its rich content and the fantastically detailed visuals, it is the kind of work that one returns to multiple times, and that stays with the reader long after it is finished. This book will appeal not only to those of Native descent, but to anyone who has sought to connect to their own identity, and to other human beings.Highly Recommended: 4 out of 4Reviewer: Colette LeungColette Leung is a graduate student at the University of Alberta, working in the fields of Library and Information science and Humanities Computing who loves reading, cats, and tea. Her research interests focus around how digital tools can be used to explore fields such as literature, language, and history in new and innovative ways.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".