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Record W2470882628 · doi:10.14288/1.0092512

Evolution and memory in a heritage landscape

2010· article· en· W2470882628 on OpenAlexaboutno aff
Ann-Marie Jackson

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsSituatedCultural landscapeIndustrial heritageCultural heritageEnvironmental ethicsGeographyCultural heritage managementField (mathematics)Landscape archaeologyEnvironmental resource managementHistoryArchaeologyLandscape designComputer science

Abstract

fetched live from OpenAlex

Heritage preservation has become a major industry and pastime in North America and Europe. While the preservation movement has traditionally focussed on architectural structures, in recent decades heritage landscapes have been recognized for the wealth of historical, cultural, economic, educational, and ecological information about both the past and the present that they contain. Time and change are critical aspects of the landscape, but tend to be addressed inadequately in heritage landscape preservation practice and guidelines. This is demonstrated by the two disparate approaches, scientific and situated, to heritage landscapes in the field of landscape architecture. This thesis examines the origins, motivations, benefits, issues, and existing Canadian and U S guidelines for the preservation of heritage landscapes, and concludes that an approach that emphasizes memory and evolution of the landscape over static guidelines will create more robust and meaningful places.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.019
Scholarly communication0.0080.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.160
Teacher spread0.143 · 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 designQualitative
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

Citations0
Published2010
Admission routes1
Has abstractyes

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