The Political Economy of Social Data
Bibliographic record
Abstract
Social media platform-industry partnerships are essential to understanding the politics and economics of social data circulating among platforms and third parties. Using Facebook as a case study, this paper develops a novel methodology for empirically surveying the historical dynamics of social media industry partnerships and partner programs. Facebook is particularly emblematic as one of the few dominant actors that functions both as data aggregator and as digital marketing platform whilst operating a multiplicity of dedicated partner programs that cater to a wide array of industry partners. We employ mixed methods by aligning digital historical research and interview methods: using "digital methods", we reconstruct both ongoing and former declared platform--industry partnerships and programs with web data whilst conducting semi-structured interviews with selected platform partners to contextualize the empirical research. This enables us to address (i) the dynamic relations between social media platforms and industry partners, (ii) their diversification by catering to a growing number of stakeholders with distinct interests, and (iii) their gradual entrenchment as dominant actors within an emerging digital marketing ecosystem. By tracing how and when partnerships and industry alliances are forged, sustained, and terminated over time we are able to develop a critical account of the political economy of social data that addresses the politics of platforms and stakeholders as well as the consolidation of platform power.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".