Sponsored Content as an Epistemic Market Object: How Platformization of Brand–Creator Partnerships Disrupts Valuation, Coproduction, and the Relationship Between Market Actors
Bibliographic record
Abstract
Sponsored content allows brands to partner with creators to reach creators’ audiences on digital platforms. However, both creators’ and brands’ incomplete understanding of this object generates two critical ambiguities: how to determine the value of sponsored content and how to effectively coproduce it. To better understand these ambiguities, the authors theorize sponsored content as an epistemic market object : an object that facilitates marketing functions but is only partially understood by the actors who use it . They analyze a dataset of interviews, podcasts, media articles, and third-party platform reviews about—and by—content creators, brands, and intermediaries. The findings show that brands, creators, and intermediaries create and apply knowledge to address valuation and coproduction ambiguities. However, this knowledge work is incomplete, creating asymmetries in value outcomes and power relationships in a brand–creator partnership. This research contributes to marketing literature and practice by highlighting the role of epistemic market objects in transformative market disruptions that alter the roles of, and the relationships between, market actors. The findings are transferable to other substantive areas such as generative artificial intelligence, the metaverse, nonfungible tokens, online news, and the sharing economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".