Mapping mental barriers that prevent the use of neighborhood green spaces
Bibliographic record
Abstract
In comparison to the study of green space use, the study of its non-use or rejection is greatly understudied.Neighborhood managers and members of local gardening initiatives of Halle-Newtown, Germany, state that residents ignore local green-blue infrastructure (GBI) for recreational use.Halle-Newtown is a former showcase, large prefabricated socialist housing estate that is now facing an increase of households deprived in multiple ways.We are interested in the question of why people of Halle-Newtown refuse to use local GBI.In order to uncover potential barriers to the enjoyment of the ecosystem service benefits of local GBI, we have chosen the method of mental mapping to explore place attachment in Halle-Newtown.In summer 2018, about 100 residents of Halle-Newtown described the places they prefer when relaxing from a stressful and hot summer day.The results were surprising.Local GBI, be it created in socialist times or recently, was completely absent from their mental maps.Instead, people would overcome longer distances and cover higher costs to reach central green spaces.Tacit knowledge, namely the untold general rejection of the entire neighborhood by the residents, was found to be the deeper reason behind non-use of GBI and missing place attachment.The results uncovered that both neighborhood neglect and the multi-scalar character of urban recreational ideas/behavior are factors that help us to understand nonuse of urban GBI, two key insights for urban planning.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".