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Enregistrement W7103514129

Deep Mapping the Biome: The Biology of Place in\nDon Gayton's <i>The Wheatgrass Mechanism</i>\nand John Janovy Jr.'s <i>Dunwoody Pond</i>

2005· article· W7103514129 sur OpenAlexaboutno aff

Notice bibliographique

RevueInsecta mundi · 2005
Typearticle
Langue
DomaineArts and Humanities
ThématiqueEcocriticism and Environmental Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDistrustVisionNarrativeDeep learningNatural (archaeology)Deep time
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In his influential review, "Deep Maps in Ecoliterature," scholar Randall Roorda argues that the deep map deserves status as an "incipient genre of environmental literature," noting its "ambitious ... self-reflexive" nature and its innovative narrative architecture. The term "deep map" itself is the invention of writer William Least Heat-Moon, whose extended essay PrairyErth (a deep map) has given definition to this form. Deep-map writing is marked by its intertextual, interdisciplinary, and multivocal nature. It is also self-consciously cartographic, presenting maps, following maps, and redrawing maps. Deep mappers "both distrust maps and rely on them," or as Heat-Moon puts it, they "test the grid." As he follows the twenty-five United States Geological Survey maps of Chase County, Kansas, Heat-Moon plumbs natural and human history, seeking the vestiges and expressions of this deep history in his walks around the county, but also seeking from others' published texts, scientific inquiry, and oral history an exhaustive accounting of place. Prompts from his own dreams and visions also inform his narrative. Heat-Moon's accomplishment in PrairyErth, reminiscent of Henry David Thoreau's Walden, has earned him much praise. Lawrence Buell proclaims PrairyErth "perhaps the most ambitious literary reconstruction of a small portion of America ever attempted in a single volume." This textual achievement has provided an emerging form of environmental writing, created an alignment among essayists experimenting with intercalated form, and inspired Heat-Moon's contemporaries to attempt their own deep maps.\nYet Heat-Moon did not invent the form. Another Plains writer, Wallace Stegner, prepared the ground for the deep-map years earlier with the 1962 publication of Wolf Willow, the urtext of all deep maps. In a sweeping narrative that encompasses geology, climatology, botany, political and settlement history, fiction, memoir, and myth, Stegner chronicles the Cypress Hills, fictional Whitemud, Saskatchewan (Eastend), and the Medicine Line. If the deep map is only now "incipient," it has had a long gestation, and its origins lie in the Great Plains straddling both sides of the forty-ninth parallel. Between 1962 and 1991, nonfiction writers were experimenting with the representation of place, and the deep map gave them both a new aesthetic and a land ethic. The significant features of this genre include its multivalent, cross-sectional understanding of history; its attention to the environment and advocacy of bioregionalism; its amalgamation of genres (e.g., Stegner's subtitle A History, a Story, and a Memory of the Last Plains Frontier); and its interest in a specific place, a particular biome, or a unique landscape. The deep-map genre claims practitioners from many disciplines-journalism, poetry, science, and ranching, for instancefor a reason: the flexibility of a cross-genre, cross-sectional narrative provides a format that revels in the nuances and variables of idiosyncratic experience, training, and appreciation of landscape. This form of literary stratigraphy shapes a distinctive kind of "articulated geography." That writers as diverse as Barry Lopez, John McPhee, and Sheila Nickerson, for instance, stand alongside Stegner and HeatMoon as deep mappers, signals the growing appeal of this genre. Buell has argued that environmental literature in general "increases our feel for both places previously unknown and places known but never so deeply felt." The deep map makes the "deeply felt" its forte.

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict), Études des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,963
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,004
Communication savante0,0010,001
Science ouverte0,0030,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 GPT0,212
Écart entre enseignants0,195 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

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
Publié2005
Routes d'admission1
Résumé présentoui

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