Economic globalisation and inclusive green growth in Africa: Contingencies and policy‐relevant thresholds of governance
Bibliographic record
Abstract
Abstract This study employs macrodata for 23 African countries to examine whether good governance interacts with economic globalisation (EG) to foster inclusive green growth (IGG). First, the study finds that EG hampers IGG in Africa. Second, although unconditionally good governance promotes IGG, only government effectiveness interacts with EG to foster IGG. Across the social and environmental sustainability dimensions of IGG, however, the effects differ substantially. Notably, whilst the EG‐governance pathways yield remarkable environmental sustainability net gains, a modest harmful effect was observed for socioeconomic sustainability. Evidence from our threshold analyses also suggests that whilst government effectiveness is critical for propelling EG to promote IGG, across the social and environmental perspectives of IGG, it is investments in building frameworks and structures for corruption control and the rule of law that are crucial. Our results shed new light on IGG and have several implications for Agenda 2030 and Agenda 2063.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".