Chasing the Mainstream: Corporate Social Advocacy and Online Content Governance
Bibliographic record
Abstract
Expectations that commercial actors will act responsibly and for the public good have precipitated a growing number of public controversies as companies either line up to demonstrate their commitment by taking sides on a social issue or are forced to respond to public pressure. These controversies can have knock-on effects for the governance of content and expression, as what is tolerated can easily shift in the face of the intense public criticism that surrounds these controversies. Media and communications research has speculated on the role of the profit-motive in content moderation on social media but there are gaps in research directly addressing how the advertising industry attempts to institutionalize its interests in appropriate content, as well as in research extending the examination of professional communications beyond advertising-supported media to the wider environment of online content moderation. Given the high stakes of public interventions by commercial actors, a better understanding of their motivations and the regulatory implications of their actions is needed. This dissertation responds to the questions, what motivates corporate social advocacy? How does information circulate in advocacy events? What issues are compatible with corporate social advocacy? And what role does corporate social advocacy play towards governing online content? To answer these questions, it analyzes three public controversies over online content, representing the advertising-supported environment of social media and non-advertising supported environments such as online infrastructure services and gaming and provides a thematic analysis of the professional communications industry perspective on social advocacy controversy. It takes a framework based in a functional public sphere and draws on theories of economies of worth to position corporate social advocacies as “tests of justification” that are revealing of the normative standards applied to commercial advocacy and governance of content. It finds that while controversies can push companies beyond “owned” issues and change the treatment of certain content, the resource intensive and publicly damaging nature of the controversies incentivise controversy-averse approach to moderation, which can chill the creation of content seen as outside the mainstream.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| 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".