Intervention levers for increasing social acceptance of conservation measures on private land: a systematic literature review and comprehensive typology
Bibliographic record
Abstract
Abstract Private lands are increasingly targeted for ecological restoration and conservation initiatives in high-income countries. However, the fragmented nature of private land tenure, the large number of landowners and their heterogeneous profiles can pose significant challenges for conservation initiatives. This can lead to a range in landowners’ attitudes toward conservation initiatives, with some initiatives being received with resistance, and others with consent and participation. Most research dealing with social outcomes of conservation or restoration initiatives on private lands addresses regionally specific case studies, but few studies have attempted to derive general trends. To fill this gap, we performed a systematic literature review of conservation measures on private lands to develop a comprehensive typology of factors influencing the acceptance of conservation initiatives on private lands. Our results show that conservation agents (typically government agencies or NGOs), despite their limited power over individual factors of private landowners, can seek to encourage both the adoption and perceptions of conservation initiatives on private land through improving institutional interactions. We propose six recommendations to help support and design conservation programs on private lands and to identify intervention levers that may be acted upon to improve the social acceptance of such conservation initiatives.
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 | 0.030 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".