Creating content, influencing democracy: situating corporate political communication between marketing and activism
Bibliographic record
Abstract
This thesis investigates the commercial backlash to a contentious piece of legislation in North Carolina and considers the implications of commercial incorporation of political content on public service communications. Commercial actors have high social standing and a privileged relationship with the social platforms used to disseminate much of commercial speech. In their pursuit of direct relationships with consumers, unmediated by publishers and free of the distrust of advertisement that has often characterized consumer-marketer relations, brands have cultivated content marketing practices based on serving consumer interests. Access to analytical tools allows companies to evaluate the success of content, eventually creating an environment in which most companies feel comfortable taking political stands in a way they did not before the widespread adoption of social media. Closer examination of political speech by commercial entities reveals strategies of communication that undermine avenues for public exchange and short-circuit non-market means of protest.
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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 | 0.008 | 0.016 |
| 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 | 0.026 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".