Apportionment of Funds to Mining-Impacted Communities within Forest Reserve Environments in Ghana
Bibliographic record
Abstract
This paper uses risk assessment of socio-economic and environmental indicators to develop criteria for apportionment (AP) of funds to communities impacted by mining activities within a Forest Reserve environment in Ghana. A t-test statistic of assessment data shows that factors such as; cost of living, health conditions, air pollution, water contamination, land degradation and distance (D km) are significant. Ratings of the factors using a 1-5 intensity scale to determine risk values (RV) for communities indicate that RV of 25 requires no remediation, while (25-RV) is remediation factor. A long-term AP criterion using the ranked risk (RR) values is defined by the equation APRR%=7.45-0.233(RR) with R2 of 0.961. Based on this criterion, communities D and S which are located at 1.5 and 16.1km from the mining focus have APRR values of 7.23% and 2.26%, respectively. APRR% in relation to distance is defined by the equation APRR%=7.22-0.281(D) with R2 of 0.647. RR-dependent distance equation (APD %) is used to deduce a short-term criterion defined by K*APD%=4.13-0.097(RR), where K is a constant. The expression of (APRR-K*APD) represents the residual apportionment required after a long-term assessment. The findings in this research demonstrate well-defined patterns that can be replicated for similar future projects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".