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Record W2619964188 · doi:10.1017/aaq.2017.26

SAVAGE LANDS, CIVILIZING APPETITES: POWER AND WILDERNESS IN QUEBEC CITY (1535–1900)

2017· article· en· W2619964188 on OpenAlexaboutno aff
Julie-Anne Bouchard-Perron

Bibliographic record

VenueAmerican Antiquity · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsWildernessIndigenousPoliticsNatural (archaeology)GeographyConsumption (sociology)Power (physics)EthnologyPerspective (graphical)Cultural landscapeNatural landscapeHistoryCultural heritageNatural resourceArchaeologyPolitical scienceEcologySociologySocial scienceArtLaw

Abstract

fetched live from OpenAlex

Quebec City (Canada) is well-known for its unique wild environments and its protected cultural heritage, which have attracted tourists to the area for centuries. Often taken for granted or overlooked, the interactions between its natural and cultural landscape have been scantly investigated in a diachronic perspective in archaeology. This article explores the relationship between Quebec City's colonists/inhabitants and their surrounding territory, through the study of food plants for the period between 1535 and 1900. It contrasts visitors’ accounts and official discourses with daily experience using a combination of historical and archaeobotanical evidence to unravel how changing historical, social, and political contingencies impacted past perceptions of “wilderness” as manifested in food choices. In this process, the consumption of local indigenous plants has been interpreted as a form of incorporation of local environments that can enlighten us about popular attitudes toward the New World's natural features.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2017
Admission routes1
Has abstractyes

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