Social acceptability of a wind turbine blade facility in Kingston upon hull
Bibliographic record
Abstract
In November 2016, Siemens Gamesa started construction of its £310-million, off-shore wind turbine blade assembly facility in the city of Kingston upon Hull in the UK. This paper adopted a mixed method approach, that is, maps, charts and tables and meta analysis to investigate the social acceptability of local residents to such investments using feedback from three residents’ surveys conducted over a period of nearly 3 years. The study is a first of its kind as it presents a real case study of social acceptability of a large manufacturing facility, located close to a residential area, that significantly changed the landscape of the area. The findings indicate that residents on the whole favour such investments because of the economic opportunities. For instance, over 1000 direct jobs were created with the Office for National Statistics reporting a growth of 4.2% in Kingston upon Hull's economic output in 2016–2018. Similarly, Demos-PwC Growth for Cities Index 2018 ranked it, the third-most improved UK city to live and work. However, there were some concerns especially from those living close to the facility regarding issues like noise from ships docking and loading during the night and the obstruction of the scenery of the estuary by an erected sound barrier. The study also shows that it is important for the investors to work closely with local stakeholders and residents to maximise the returns whiles minimising the negatives.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".