Dreaming in Indian: Contemporary Native American Voices, ed. by L. Charleyboy & M. Leatherdale
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».