A Liberal Egalitarian Perspective on the Platform Economy: Mitigating its Distributive Effects or Changing the Organizations Running it?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
I argue that a just regulation of new clubs and online platforms might require both changing the kind of organizations running them and implementing mitigating policies to compensate the negative effects of market disruptions. The first contribution consists in explaining how theories of organizations can help to understand two important economic processes facilitated by information technologies like online platforms. This subsequently allows me to untangle various ways in which these processes create unjust inequalities. The second contribution consists in distinguishing two distributive strategies to tackle resulting unjust inequalities. According to the mitigating strategy, public institutions should tolerate all kinds of organizations running clubs and platforms and limit their intervention to mitigating policies such as redistributive taxation and adapted social protections. The organizational strategy goes further. In addition to previous mitigating policies, public institutions should also change the kind of organizations running clubs and platforms. They should promote cooperatives of contractors and users, for instance, to compete with current investor-owned firms and to ultimately run the platform economy in their stead. The third contribution consists in discussing the pros and cons of each strategy. In particular, I raise a challenge to the organizational strategy and I outline the kind of arguments needed to respond to it. This suggests that the organizational strategy could be justified but demonstrating this requires more research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it