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Record W619387146 · doi:10.30861/9781407303208

A Critical Exploration of Frameworks for Assessing the Significance of New Zealand’s Historic Heritage

2008· book· en· W619387146 on OpenAlexaboutno aff
Sara Donaghey

Bibliographic record

VenueBAR Publishing eBooks · 2008
Typebook
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Cultural heritageIndustrial heritageEnvironmental planningEnvironmental ethicsCultural heritage managementEnvironmental resource managementHistoryGeographyPolitical scienceArchaeologyComputer scienceEnvironmental sciencePhilosophy

Abstract

fetched live from OpenAlex

This study argues that considerations of value and significance are fundamental to sustainable heritage management practice. It explores critical issues relating to the valorisation of historic heritage in New Zealand and considers whether existing frameworks for evaluation and assessment are effective and appropriate. The two frames of reference comprise: firstly, theoretical principles relating to the nature and qualities of heritage value and secondly, operational strategies relating to the process ofassessment. The study integrates current policy and practice within existing epistemology with primary research data using a mixed methodology. A review of international policy and practice contrasts the various approaches used in Australia,Canada, England and the United States of America, and identifies effective system characteristics. Existing understandings and practice within New Zealand are considered and analogies made between particular elements of the primary research drawn from surveys of professional and non-professional opinion of the heritage assessment process. The New Zealand findings are then set against the review of international evidence and the literature to identify significant strengths and shortcomings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.158
GPT teacher head0.285
Teacher spread0.126 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2008
Admission routes1
Has abstractyes

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