Gender inequalities in Transylvania's largest peri-urban forest usage
Bibliographic record
Abstract
Urban green spaces (public gardens, parks, urban and peri-urban forests) offer multiple-use opportunities and spaces for recreational activities and played a key role in supporting mental and physical health of dwellers during covid-19 pandemic, being ones of few places where outdoor and social activities where allowed. This study was conducted in Brașov city (also known as Kronstadt, by its German name), the second largest metropolitan area of Romania and surrounded by a significant area of peri-urban forests in Transylvania. Brașov city own just 5.62 sqm of urban green space/inhabitant, one of the lowest in the country, so the presence of a large peri-urban forest area become very valuable for locals and tourists visiting the area. Due to its importance and because understanding visitors' expectations and perceptions is a key element to support decision-makers and ensure proper management of these forests, the Brașov's forests administrator (Kronstadt Local Public Forest District – RPLPK) decided to investigate how dwellers generally interact with the peri-urban forests and to identify opportunities for improving the capacity of forests in providing social and recreational services. Data were collected through the administration of CAWI (computer assisted web interview) to 314 respondents at beginning of 2021, at exactly one year distance after the pandemic lockdown was imposed all around the country. Analyzing the participants responses, a surprising fact become evident: the use of peri-urban forest is not gender equal, women being less able than men to access these green natural spaces and, therefore, to uptake the benefits provided by the peri-urban forests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".