Democratic legitimacy in global platform governance
Bibliographic record
Abstract
The goal of this paper is to propose a democratic legitimacy framework for evaluating platform-goverance proposals, and in doing so clarify terms of debate in this area, allowing for more nuanced policy assessments. It applies a democratic legitimacy framework originally created to assess the European Union's democratic bona fides – Vivian Schmidt's (2013) modification of Scharpf's (1999) well-known taxonomy of forms of democratic legitimacy – to various representative platform governance proposals and policies. The first section discusses briefly the issue of legitimacy in internet and platform governance, while the second outlines our analytical framework. The second section describes the three forms of legitimacy that, according to this framework, are necessary for democratic legitimation: input, throughput and output legitimacy. The third section demonstrates our framework's utility by applying it to four paradigmatic proposals/regimes: Facebook's Oversight Board (self-governance regimes); adjudication-focused proposals such as the Manila Principles for Intermediary Liability (rule-of-law-focused regimes); the human-rights-focused framework proposed by then-UN Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression; and the United Kingdom's Online Harms White Paper (domestic regime). Section four describes our four main findings regarding the case studies: non-state proposals seem to focus on throughput legitimacy; input legitimacy requirements are frequently under examined; state regulation is usually side-lined as a policy option; and output legitimacy is a limited standard to be adopted in supranational contexts. We conclude that only by considering legitimacy as a multifaceted phenomenon based in democratic accountability will it be possible to design platform-governance models that will not only stand the test of time, but will also be accepted by the people whose lives they affect.
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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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".