Bibliographic record
Abstract
I presented my paper before members of the Canadian Communication Association at its annual conference at the 2009 Congress of the Humanities and Social Sciences held this past May at Carleton University in Ottawa. \n \nParticipants were interested in the results that I presented about an experiment that I designed to capture the effects on college women’s body esteem of contravening pro-esteem and pro-thin messages broadcast by TV ads and movies. \n \nThey asked questions about the influence of media messages on young women, younger than the cross-section I examined in this experiment, as well as on older women. I cited various experimental research results to provide them answers to these questions. \n \nThey also asked questions regarding my choice of closed and open questions in the instrument I designed to capture changes in female viewers’ attitudes to media and body esteem. \n \nThey suggested that I consider running in-depth interviews of female viewers’ thoughts regarding test messages they viewed. I explained that statistical analyses of close-ended questions were necessar in this experiment to reveal a correspondence between viewers’ conscious and unconscious thoughts about test messages and corresponding body-image changes. I also explained that coding viewers’ short written answers to open-ended questions revealed patterns in their thoughts that highlighted the effects that Petty and Cacioppo’s Elaboration Likelihood Model (1986) predicted regarding the durability of persuasion effects. Coding viewers’ short answer open questions had helped me correlate data in this experiment that linked attitudes to media and body image changes more concisely than long interview responses could have. \n \nParticipants in my presentation also questioned me about the direction that my future research would take and appreciated my responses. I indicated to them that I need to “adjust” my research instruments by designing new questionnaires that can better help me test the predictive power of persuasion theory and reveal how viewers’ attentiveness to message cues engages them in a comparative-thinking process (Festinger, 1954) as well as in social-relational thinking (Leary, 1999) affecting their body esteem. \n \nAs well, they appreciated hearing about I plan to recruit female participants in greater numbers and run future experiments via the Internet. By taking this initiative on-line, I hope to help pioneer methods of research that Athabasca University’s future Virtual Media Laboratory can utilize. I also hope to help establish our University as a leader in on-line research I wish to involve our graduate students in recruiting, briefing, testing, and debriefing research participants. We need our communication students to help analyze data from this and other on-line communication research projects in order to help disseminate similar research results on on-going basis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.012 |
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".