Native advertising disclosures in journalism: An assessment on the accurate reporting of disclosure wording in conveying advertising intent
Bibliographic record
Abstract
The struggling journalism industry adopted the practice of native advertising to raise digital revenue. This practice offered advertisers a chance to purchase the services of a publication in order to have their story published. The goal of native advertising is for advertising to become invisible to consumers, and to be presented to audiences as if were regular editorial content. The only distinguishing feature is a disclosure, often identifying the accompanying article as being “Sponsored Content,” “Promoted Content”, “Custom Content,” or a “Paid Post.” This research paper discusses the struggles of journalism and digital advertising. It examines the many definitions of native advertising, and the advertising theory of the cool sell, in which advertising moves away from clearly demarcated interruptions and hence disappears from the public eye. It also examines the ethical implications and the possibility of deceiving audiences by presenting adverting as if it were editorial content. The focus of this research paper is in the very disclosures that act as the separation between editorial and advertising content. A total of 688 undergraduate students at the University of Windsor participated in an online survey designed to determine if they could accurately assess the reporting intent of the various disclosures using an even-point Likert scale. Survey participants viewed two native advertisements, each with a randomized disclosure, and answered key questions as to whether they were able to perceive the advertising intent of the article. Results of the study proved inconclusive in determining whether any single disclosure was more effective than any other. This may be attributed to the various challenges in studying native advertising and indicates that perhaps we need to move beyond studying the disclosures and focus more on the ethical issues of the practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.089 | 0.316 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".