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Record W2154657954 · doi:10.3368/lj.34.1.79

Wider Horizons of American Landscape

2015· article· en· W2154657954 on OpenAlexaboutno aff
B. N. K. Davis

Bibliographic record

VenueLandscape Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSyncretism (linguistics)Latin AmericansIndigenousScope (computer science)Context (archaeology)Landscape architectureEnvironmental ethicsPerspective (graphical)Pluralism (philosophy)Conceptual frameworkSociologyHistoryGeographyPolitical scienceEpistemologySocial scienceArchaeologyEngineeringEcologyVisual artsCivil engineeringArtLawLinguistics

Abstract

fetched live from OpenAlex

This article situates contemporary landscape architecture in the United States and Canada as part of a long, rich tradition of landscape- making found throughout the Americas. This perspective calls for a wider scope and conceptual framework for renewed, vigorous, and sustained engagement with indigenous and Latin American landscapes. In this article, I pursue this by blending hemispheric studies with landscape architecture to study historical and contemporary sites, projects, practices, and theories of Latin American landscape within a broader hemispheric context. The piece begins by introducing the field of hemispheric studies and assembling methods suited to the undertaking. The second section addresses the question of origins and shows that pluralism and syncretism are critical to understanding American landscapes. I then draw from existing literature and my own fieldwork to survey contemporary conditions and develop four concepts for the study of American landscapes, before finishing with conclusions intended to serve as guideposts for future work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.018
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.032
GPT teacher head0.328
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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