Preferences for Rural Living: Naturbanization Versus Accessibility
Bibliographic record
Abstract
This paper aims to determine whether the urban sprawl onto the rustic lands of the Urdaibai Biosphere Reserve (UBR) is driven by the environmental and landscape qualities of this protected natural area and can be defined as “naturbanization”. Aware that residential choice factors are both complex and multidirectional, we have taken, as a comparison scenario, the unprotected rural area which borders with the Reserve (Ex UBR). This enables us to determine whether the housing preferences of new buyers are predominantly driven by the “reserve effect” (naturbanization), or by the appeal of the neighbouring unprotected area which is closer and better communicated to the city (accessibility) and presents less stringent building regulations. Our findings for the UBR reveal a “reserve effect” that would support the naturbanization hypothesis, but the results obtained in both property markets show that the price-boosting impact of the “accessibility/proximity effect” in unprotected rural land is stronger than that of the UBR “reserve/naturbanization effect”. Statistical tests conducted on the variables that determine urban sprawl into the non-developable rustic land of protected and unprotected areas serve to establish a definition/characterization of naturbanization that transcends the local/particular and applies to the general, becoming a small theoretical contribution on this issue. We conclude that naturbanization is characterized by factors that influence residential preferences of property buyers (house+rustic land) for protected natural areas. What gives naturbanization a distinctive characteristic is the subjection of such protected areas to specific conservation regulations that restrict choices and decisions of prospective buyers. These facts enrich our understanding of the tradeoffs between nature protection policies and economic development in these areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".