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Enregistrement W2801787911 · doi:10.1353/mwr.2018.0027

Deep Map Country: Literary Cartography of the Great Plains by Susan Naramore Maher

2018· article· en· W2801787911 sur OpenAlexaboutno aff
Matthew S. Luckett

Notice bibliographique

RevueMiddle West review · 2018
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueArchaeology and Natural History
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGeographyHistoryCartographyArt historyArchaeology

Résumé

récupéré en direct d'OpenAlex

Reviewed by: Deep Map Country: Literary Cartography of the Great Plains by Susan Naramore Maher Matthew S. Luckett Susan Naramore Maher, Deep Map Country: Literary Cartography of the Great Plains. Lincoln: University of Nebraska Press, 2014. 256 pp. $45. American road trip passengers traversing the Great Plains have surely welcomed the invention of smart phones and seatback DVD players. Long derided as flat and boring, the Plains are too often seen as an unnecessary geographic obstacle to ostensibly more interesting geographic regions to the east and west. Similarly, certainly millions of airline passengers over the past several decades have opened up their airplane windows above Kansas or Nebraska, looked down upon the farms and villages peppering an end-less array of large green and brown squares, and quickly returned to their in-flight magazines. Those travelers are missing out. The region’s flat horizons belie its depths and hidden worlds, tricking visitors into not noticing the biome’s complex ecology, the landscape’s violent geological and historical past, and the land’s stubborn refusal to behave according to colonists’ expectations. Perhaps that is why, according to Susan Naramore Maher, Great Plains writers and scholars have pioneered “deep mapping,” or literary cartography, in their works. Literary cartography seeks to express, explore, accentuate, and add dimensionality to places that often seem blank or empty on maps, and featureless to newcomers. Maher’s study of this genre’s imprint on the region, [End Page 105] Deep Map Country: Literary Cartography on the Great Plains, is an essential synthesis to a body of work that transcends and frequently ignores disciplinary, cultural, and national boundaries. This study is organized thematically, beginning with first chapter introduction based on her 2001 article in Western American Literature that explains the concept of deep-mapping and its regional context. The four chapters that follow are each built around two or three works whose comparisons provide the substance of Maher’s discussion. Wallace Stegner’s Wolf Willow and William Least Heat-Moon’s PrairyErth exemplify efforts to recover deep map histories of the Plains from Saskatchewan to Kansas in Chapter Two. Maher then juxtaposes human settlement against Plains ecology in the third chapter, whose analysis of Don Gayton’s The Wheatgrass Mechanism, John Janovy Jr.’s Dunwoody Pond, and Wes Jackson’s Becoming Native to this Place illustrates their approach from “the edges [of the Plains], sharing the bounty of their research, yet understanding that science, by itself, is not enough to guide communities into settled futures” (28). Chapter Four discusses the work of deep time excavators John McPhee and Loren Eiseley, whose books Rising from the Plains and The Immense Journey, respectively, tell the grand story of the region’s geological past. Finally, Chapter Five investigates the experiential Plains through the works of Julene Bair’s One Degree West, Sharon Butala’s Wild Stone Heart, and Linda Hasselstrom’s Feels Like Far. Many readers may already be familiar with Stegner, McPhee, and other writers featured in Maher’s discussion. But for anyone unfamiliar with literary cartography as a transformative, cumulative literary genre, this book does a vital service. The discursive works surveyed here stand alone, and are in many ways idiosyncratic within their own literary and scientific niches. But as Maher demonstrates these works are in dialogue with one another, and their approaches emanate from a set of common concerns that the Plains are, popularly boring, endlessly fascinating, and existentially threatened by human overuse. These themes all figure prominently in the conclusion, which narrates both the recent resurgence of interest in the Plains among writers and their varied attempts to save Plains communities as they hemorrhage population. While Maher’s monograph is a welcome and necessary introduction to the rhizomatic world of deep mapping and its didactic application to the [End Page 106] Great Plains, maps and illustrations would help the reader better visualize this intricately layered world. This reviewer, a Great Plains scholar and longtime visitor, had no trouble imagining the historical, geological, and cultural layering of small towns and fissures in the earth, but what about the historian in Boston or the English professor in Berkeley? As important as this book is, it should be more accessible...

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,523
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,002
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,270
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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