Platform developmentalism: leveraging platform innovation for national development in Latin America
Bibliographic record
Abstract
Recently, development scholars have begun to study the platform economy.In Latin America, platformisation has resulted in the widespread reorganisation of business practices across many sectors, with important implications for incumbent industries, labour and social processes.These changes raise questions about the potential contributions of platformisation to national economic health and social welfare.This paper argues that the link between platformisation and development can be studied from a developmental state point of view.Specifically, in Latin America, the disruptions caused by platform innovations create policy windows that could result in platform developmental policy innovations, however, developmental policy-making is constrained by the structural characteristics of Latin American economies.Taking this into consideration, the paper positions Biber et al. 's (2017) model of policy disruption, and Fairfield's (2015) model of policy influence as tools to critically assess platform policymaking from a developmentalist point of view.This approach is illustrated through a survey and discussion of policy disruptions caused by platformisation in the transportation, lodging and fintech sectors of Chile, Colombia, Mexico and Peru.The discussion surfaces specific challenges for platform developmentalism related to policy autonomy and capture, societal mobilisation of data and other resources, and state-market collaborations.The paper concludes by positioning the 'platform society' as a normative goal and offers an agenda to advance it through comparative research of platform policymaking.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".