Mapping community knowledge of vulnerability of value: a case study in the UK
Bibliographic record
Abstract
Commercial property value can be vulnerable to risk exposure, economic effects of risk on property and perception of usefulness of the property in the market.The processes of decision making in risk reduction can generate differential exposure to risk making some organisations more vulnerable than others.This research deals with understanding exposure of property value to flood risk in a selected case study area in Wakefield, England.The work constitutes identification of variables that analyses the business vulnerability of organisations and presenting them through an operational framework.The operational framework is then practically tested in the field using the questionnaire survey method.The results from the survey show the differential attitude of respondents with varying levels of knowledge and occupational experience towards specific factors associated with flooding that may affect property value.However, a tendency can be observed for flooding and its effects to be taken more seriously in peoples' perception.It was difficult to observe direct evidence of effect of flooding on commercial property price or rent, but it can be noticed that respondents from all flood hazard zones recognised flooding as an issue of concern and emphasised problems of 'loss of income' and the requirement for 'cheap and easily available insurance'.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".