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Record W2092277788 · doi:10.1080/09640568.2013.815606

Contextualising site factors for feasibility analysis

2013· article· en· W2092277788 on OpenAlexaff
Russell R. Currie, Franz Wesley, Gurupdesh S. Pandher

Bibliographic record

VenueJournal of Environmental Planning and Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Land Suitability Analysis
Canadian institutionsUniversity of WindsorMcGill UniversityThompson Rivers University
Fundersnot available
KeywordsStakeholderContext (archaeology)Identification (biology)Site planningComputer scienceRepresentation (politics)Risk analysis (engineering)Decision analysisSite selectionOperations researchRelation (database)Management scienceEngineeringData miningBusinessGeographyCivil engineering

Abstract

fetched live from OpenAlex

This paper explores the utility of site analysis as one factor in determining the feasibility of a proposed development in relation to organisational objectives. Feasibility analysis models frequently include site analysis as one factor in the broader study. However, site analysis for site planning and design is generally presented under the assumptions of a more advanced stage of planning than can be admitted by the constraints imposed by a feasibility analysis in the pre-start up phase of a proposed development. Site analysis in the context of feasibility analysis requires a model that emphasises its capacity for making a ‘go/no go’ decision on a proposed development programme based on uncertainty, limited resources and multiple stakeholder interests. From the multiple criteria decision-making literature a method is developed and applied to determine the fitness of a site for supporting a proposed tourism development. Moreover, the proposed site analysis matrix and coding scheme provides practitioners with parameters that can inform subsequent site planning actions. While application of the concept bears limitations in quantitative measurement and spatial representation, the results suggest the proposed method for site analysis is beneficial and useful in the context of feasibility analysis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

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)0.0010.000

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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 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

Citations3
Published2013
Admission routes1
Has abstractyes

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