Bibliographic record
Abstract
The aim of the article is to identify problems related to the siting of wind farms, both those that have arisen as a result of recent legislative revisions and those arising from social developments in Poland. In 2022 a map defining ‘exclusion zones’ around wind turbines, i.e., areas where residential development was prohibited, was released in Poland. It was only then that many territorial governments realised the scale of the problems generated by the entry into force of the 2016 Wind Farm Act. It turned out that this group of municipalities included towns that might suffer some consequences despite the fact that there are no or few wind farms in their area. The aim of this paper is to identify towns and cities where more than one quarter of the area is land within the H10 zones, where the construction of wind farms is banned, if their distance from the nearest building or from the boundary of a national park is less than ten times the height of the turbine mast. The example of the town and rural municipality of Darłowo shows that in the early phase of their construction in Poland, wind turbines were perceived positively and did not give rise to conflicts. It was only after some time, as existing wind farms started to be expanded and new ones built, that protests emerged. The arguments of the parties to the conflict focused mainly on economic considerations: profits for the municipality versus losses for the residents neighbouring the wind turbines, such as barriers to the development of agritourism. The conflicts were fuelled and prolonged by irregularities in the municipality’s planning documents and a poor flow of information about planned investments. In 2022, The Council of Ministers adopted a draft amendment to the Wind Farm Investment Act. This legislation represents a compromise between the opportunities for wind energy development and the needs of local communities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".