Gender Difference in Sports Advertisements and Its Teaching Implications: A Systemic Functional Multimodal Discourse Analysis (SF-MDA) Approach
Bibliographic record
Abstract
The study investigates the gender difference in 20 sports ads selected from 63 sports ads obtained from the Internet. The present study focuses mainly on interpersonal meaning of the selected sports advertisements and the findings have been used to develop teaching materials in this area. The data analysis shows that men and women ads are different in a few areas, such as men are more likely portrayed as powerful, heroic, moving, and even religious images, while women are depicted as fashionable, sexy, charming and still images. Gender difference also exists in terms of the color, gaze, distance, angle, the size of frame, etc. Male and female images interact differently with verbiage found in the ads, for instance, metaphorical meaning is created by the interaction of text and image. In addition, the report concludes by considering the implications for teachers when using multisemiotic materials in order to scaffold students’ learning. The study also proposed that a joint effort should be made to help teachers teach multisemioitics.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".