Determinants of the Share of the Economy Contributed by the Forestry Industry in Ghana from 1975 to 2023
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Bibliographic record
Abstract
The macroeconomic determinants of the share of the economy contributed by the forestry industry in Ghana were examined over the period from 1975 to 2023, based on the development of time-series cointegration and error correction models. The analysis indicated that the share of the forestry industry was positively influenced by the real value of the cocoa industry, the exchange rate, and the real interest rate. The relationship between the forestry industry's share and per capita real gross domestic product (GDP) was found to be curvilinear: at low levels of per capita income, the share of the forestry industry in the economy increased with increasing income; beyond a certain level of per capita income, the share of the forestry industry declined. Additionally, economic shocks, namely the El Nino weather phenomenon, and political instability, related to the occurrence of military coups, were identified as negative influences on the share of the economy attributed to the forestry industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it