Adolescents Voice Preference in Auditory Advertisements
Bibliographic record
Abstract
This study aimed to gauge if adolescents' bias or prejudice towards a particular gender could be observed through narrator preference in auditory advertisements to ascertain if the perception of gender and its stereotypes has changed among younger generations. Prior research shows that when adult subjects are presented with multiple advertisements that they demonstrate a preference towards male narrated advertisements; however, these previous studies were performed on adults; therefore, narrator preference remains unknown for most teenagers. For this study, research data were collected through a mixed media survey in which a descriptive research process was completed. Participants in this study included 135 high school juniors and seniors both male and female. Initial results showed that statistically there was no preference for either male or female narration. From this data, one can conclude that today's teenagers do not show an overt bias for a narrator of a specific gender. Therefore, the conclusion can be drawn that the perception of gender and gender stereotypes have changed towards more egalitarian views in today's younger generations. However, this study was limited to high school-aged teenagers and did not encompass youth of all age groups. Future research should compare perceived gender stereotypes among various age groups to identify a more precise pattern of generational change of gender perception.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".