Bibliographic record
Abstract
Media and Advertising"Half the money spent on advertising is wasted," the industry quip goes, "but nobody knows which half."But other than a possible (half) waste of money, advertising also does a whole lot of other things.The problem is, nobody quite knows exactly what!This issue of the CJC explores some of the more intangible questions of representation (race, gender) as well as how ads work as emotional cues, not to mention advertising's usual rhetorical sins: lack of proof, vagueness, irrelevance, lying, and commodity fetishism.More concretely, Pénélope Daignault, Stuart Soroka, and Thierry Giasson in their study of the emotive aspects of political advertising attempt to identify the shortterm, attitudinal, physiological, and cognitive responses of individuals.Although these levels of measurement are fairly traditional ones in advertising studies, by focusing in more closely on the emotional resonances of advertising argumentation, the authors bring their work in line with the current emphasis on sentiment analysis.For her part, Anne-Marie Kinahan, in a study of washing machine ads at the beginning of the past century, offers an analysis of visual address that hinges on racialized dichotomies.By contrasting a White woman with "Aunt Salina," a Black washwoman, Kinahan shows how this campaign links Black women's work to the pre-industrial while White women are connected to technological progress through their use of the "New Century" washer.Fast-forwarding to current representations of women's work, Karen Grandy, in a Research in Brief, analyzes the coverage of women executives in the top five Canadian business publications.Not surprisingly, Grandy finds women executives to be seriously under-represented in the business magazine profiles.She also finds significant differences in the characterization of the parenthood-career relationship for male and female executives."Greenwashing"-the act of misleading consumers regarding the environmental practices of a product-is the focus of Jennifer Budinsky and Susan Bryant's examination of the powerful forces that align to create an advertising ideology reconciling capitalism and positive environmental outcomes.The principal means by which this reconciliation works are, they show, by tactics of deception (such as false health and safety reports).The article, through examining several campaigns, focuses also on what associations are made in the ads-to cleanliness, nature, safety-and their "sins of omission," such as not considering life cycle of products.Finally, and less on advertising per se than on underlying questions of media theory, Henry Svec turns to American folklorist and broadcaster Alan Lomax's work as a long-time song collector and
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.030 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.016 |
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".