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Record W2186526530 · doi:10.3138/cras.2015.s07en

Introduction – Geographies of Promise and Betrayal: Land and Place in US Studies

2015· article· en· W2186526530 on OpenAlexfundvenueaboutno aff
Art Redding

Bibliographic record

VenueCanadian Review of American Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsBetrayalHistoryPhilosophySociologyAestheticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

In various ways, the essays published in this special issue of the Canadian Review of American Studies testify to the perils, potentials, and necessities of “aestheticizing” the rapidly shifting social topographies of the United States. The concentration of immigrant and rural populations in cities during the latter half of the nineteenth century, for example, demanded the development of new practices and perceptions of affect and place, new somatic vernaculars and discursive phenomenologies, new styles and habituations that might accommodate the shocks of the new within the hoarier mythological narratives of frontier republicanism and American exceptionalism. In a more palpably destructive, perhaps, but no less dynamic fashion, the post-industrial dismantling of the securities of “place” compels scholars, artists, activists, cultural labourers of all stripes to question anew the unstable domains and terrains of American identity, questions we posed when deliberating our themes for the 2012 Canadian Association of American Studies conference on place and space in American Studies: What makes a house a home? What makes a home a good investment? What makes a real estate “bubble” burst? Who “owns” the streets? The water? The land? What makes this land your land, my land, or our land, from California to the New York Island, or beyond? How do you “occupy” Wall Street? How can you “walk for the cure”? How is land/earth/terrain understood and used? What are the distinct debates, discourses, and spatial practices that have defined American culture and society in the past, and how might they be changing today?

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)0.002
metaresearch head score (Gemma)0.005
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: Editorial · Consensus signal: none
Teacher disagreement score0.491
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0100.021
Scholarly communication0.0100.006
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0180.002

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 head0.028
GPT teacher head0.323
Teacher spread0.296 · 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
GenreEditorial

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

Citations0
Published2015
Admission routes3
Has abstractyes

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