Determinants of the Share of the Economy Contributed by the Forestry Industry in Ghana from 1975 to 2023
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".