Contextualising site factors for feasibility analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".