Platforms are People Too: Social Media Firms and International Relations
Bibliographic record
Abstract
Social media platforms have an increasingly central influence on global politics. Media of unprecedented reach, they have the power to sway elections, exacerbate societal polarization, promote or provoke conflict at all levels, and jeopardize relations between states. But what of the people who govern and oversee these platforms? For although algorithms and automation may underpin how social media content influences politics, the policies, approaches, and international relations of social media companies are directed or conducted by corporate executives and their representatives, actors who receive limited critical attention in International Relations (IR) scholarship. Combining multiple data sources, including field interviews with Meta and Twitter staff on three continents, this reflection suggests an approach to studying social media companies and their relationships to global politics that moves beyond abstraction and aggregation. Examining these actors and their internal dynamics through an organizational lens can shed fresh light on the contingent spatial, temporal, and normative drivers and enactments of their influence across the international system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".