Bibliographic record
Abstract
In the wake of historical and political events, stakeholder pressure can trigger shareholders to divest from politically incorrect markets with the goal of accomplishing socio-political change. While mainstream development research advocates for more private investor involvement in sustainable development to achieve the Sustainable Development Goals (SDG)s in funding infrastructure, health, education and climate change mitigation; the following paper introduces political divestiture as an alternative means to implement sustainable development. While foreign investment can create positive conditions for improving societal development, also politically incorrect usage of funds or regulatory lacunae can do harm in the developing world. Political divestiture is portrayed as an innovative investment allocation to maximize positive development impacts of investment whilst minimizing associated risks of politically insecure markets. In an attempt to balance risk and access to capital, political divestiture is proposed as a means to implement sustainable development by removing funds from politically incorrect regimes in order to be channeled towards socially responsible and sustainable finance solutions. Future research outlooks on political divestiture as sustainable development driver comprise the antecedents and success factors as well as institutional and international frameworks to foster Financial Social Responsibility in the long-term international development domain.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".