Community perception of forest reserve regulations enforcement in the Tano-Offin forest reserve, Ghana
Bibliographic record
Abstract
In areas of high ecological importance, regulations are required to ensure that anthropogenic land uses are sustainable. In regulating such areas , it is important to consider the perspectives of land users to evaluate how regulations are effectively being enforced to achieve desired goals. While local perspectives have proven valuable in ensuring forest regulation compliance, little is known regarding community members’ perception about forest reserve regulations enforcement. By using survey data from the Tano-Offin Forest Reserve, we develop a model in this study to examine the spatial, socioeconomic, and demographic factors that influence local perception about forest reserve regulations enforcement. The study finds that community members who reside within 0.15 km from the forest reserve (AOR = 1.669, CI = 1.358–5.252, p = 0.010) and have secondary education or more (AOR: 1.689, CI: 1.176–3.694, p = 0.022) are significantly more likely to perceive that forest reserve regulations are being enforced. Moreover, the study establishes that females (AOR: 0.574, CI: 0.211–0.862, p = 0.018) and migrants (AOR:0.575, CI:0.169–0.860, p = 0.025) are less likely to perceive that forest reserve regulations are being enforced. We suggest that land managers should take into consideration diverse significant spatial, socioeconomic, and demographic factors to assess the efficiency of enforcement of forest reserve regulations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".