Reconstructing the Social Contract Theory: Inculcating Ubi as the Antidote to Socio-Economic Inequality
Bibliographic record
Abstract
Economic insecurity and widening inequality have exposed fundamental shortcomings in modern welfare systems, calling into question the state’s ability to uphold its obligations under the social contract. While social contract theory has long provided the philosophical foundation for governance and distributive justice, existing welfare models often fail to translate these ideals into equitable economic realities. This study therefore investigates how universal basic income (UBI) can be integrated as a modern mechanism to reconstruct the social contract and restore democratic legitimacy in precarious economies. Adopting a conceptual and exploratory mixed-methods approach, the research synthesizes theoretical perspectives from Hobbes, Locke, Rousseau, and Rawls with empirical insights drawn from secondary data, expert interviews, and comparative analysis of UBI pilot programs in Finland, Canada, Alaska, Kenya, and recent initiatives in Spain and the United States. The analysis reveals strong public and expert support for UBI as a means of enhancing economic security and social justice, though concerns persist regarding fiscal sustainability and labor market impacts. Comparative findings suggest that UBI’s effects vary by context: while high-income nations report psychological and civic benefits, low-income settings show significant poverty reduction. Overall, the study concludes that UBI offers a viable framework for operationalizing the principles of social contract theory, institutionalizing economic rights, and reimagining state–citizen reciprocity for the twenty-first century.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| 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".