Platform Cooperatives and Poverty Eradication: Building on the Legacy of Johnston Birchall
Bibliographic record
Abstract
Johnston Birchall made tremendous contributions to research on cooperatives, including the contributions cooperatives can make to tackling poverty.His work on this subject was largely carried out at a time when the Millennium Development Goals were the touchstone for global efforts to address the needs of the world's poorest people.Since then, not only has the global economy been affected by digitalization, financial crises, climate change, and pandemics, the discourse on poverty has also changed due to the promulgation of the UN Sustainable Development Goals.An example of this is the connection between digital inequality and poverty.Our objective in this essay is to distill the key contributions of Birchall's work on cooperatives and poverty, position them within the evolving context of multi-dimensional global poverty and the platform economy, and chart a path forward for future research that can continue their development.We first identify Birchall's four key takeaways from his research on cooperatives and poverty reduction.We then introduce the distinguishing features of corporate platforms and summarise prior research on the link between the platform economy and poverty.We then turn to the core part of our essay, which focuses on tracing the rise of platform cooperatives and assessing whether the four takeaways fit cooperatives operating in the context of the platform economy.We identify points of convergence, and areas for further refinement and future research.We hope that this will encourage research on the potential contribution that platform cooperatives can have in addressing poverty and other societal challenges.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".