Digitalisation for a Just Social Compact: Global South Lessons from the COVID-19 Pandemic
Bibliographic record
Abstract
This policy paper responds to global and localised calls for a new social compact. It acknowledges the central role of digital inclusion and equity in mitigating the health and economic risks associated with the COVID-19 pandemic lockdowns to limit the spread of the virus. The pandemic highlighted the critical role of the digitalisation of public services and digital access to them for the effective participation of citizens in the economy and society, both during the pandemic and in postponed pandemic economic reconstruction. This digitalisation and access is important if equitable outcomes are to be achieved. The research explores the interplay between the uneven but intensifying global processes of digitalisation and datafication, the State and the ‘formalising’ effect on the significant informal sector in developing economies. As more people and firms come online, their visibility to the State is increasing; at the same time, other firms are ‘informalising’ as they start up or reconstitute themselves online. With firms being established or moving their operations online, the global landscape has been transformed into one characterised by diminished or new forms of labour, and firms operating without physical presence for taxation purposes and not subject to national law designed for the physical industrial era, nor to legal requirements to contribute to social protection for workers. Obligations for worker protection have therefore shifted to the State, which, in most Global South countries, already has a very limited resource base. Under pandemic and lockdown conditions the paper examines the potential of these developments to enhance weak state formation; improve much needed revenue generation; extend social protection to unprotected platform workers; and provide business and social relief to firms and individuals usually not visible to the State. With this Global South pandemic lens and in the context of post-pandemic reconstruction, this policy paper also assesses the role of digitalisation in reviving and renewing democratic governance for new and more equitable social compacts that can build the resilience of developing countries to better survive the next inevitable pandemic.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".