Bibliographic record
Abstract
Abstract This article examines how eliciting metaphors from multimodal commercials can facilitate a critical interpretation of advertising media, which is ubiquitous in the LL and highly manipulative. Adolescent students, a population which is particularly vulnerable to advertising’s influence, utilized the analytic tool of metaphor elicitation to abstract away from the vast multimodal information that characterizes commercials, and simplify this information in the linguistic formulation of metaphor (e.g., super-pharm is a circus). The contrived link between the given brand (e.g., Super-Pharm pharmaceutical stores) and its metaphorically-attached source domain (e.g., circus) was emphasized to increase awareness of how the commercial was structured to deceive consumers. The study evaluated the intervention using a quasi-experimental design, showing that metaphor elicitation facilitated the knowledge, critical attitudes, and responsible behavioral inclinations of participants concerning advertising media. The study suggests that using the linguistic formulation of metaphor can help adolescents critically interpret the increasingly-deceptive commercial landscape.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".