The ACE Family Parasocial Relationships and Evolving Representations of Family
Bibliographic record
Abstract
Although families have appeared in the media in various forms and mediums, representations of family now include a newer genre of social media influencers known as family influencers (Abidin, 2017). Family influencers post user-generated content (UGC) on social media platforms of and about their families and lives as a form of income to achieve micro-celebrity status (Abidin, 2017). The ACE Family is one of the most popular and subscribed to family influencers and have consistently posted videos following their lives and success over the past few years. With a multitude of different types of videos on their channel to engage their viewers, some of their videos show more of their day-to-day lives and activities. This research paper explores representations of family and elements of parasocial interaction theory with family influencers by using the ACE Family as a case study to analyze a sample of eight of their videos representing their daily lives over the course of two years from 2017 to 2018. Informed by parasocial interaction theory and previous studies, the textual and visual analysis largely consists of emergent coding to answer the research questions: (1) What elements of the Parasocial Interaction Theory and affordances of user-generated content does the ACE Family use? (2) In what ways and with what techniques does the ACE Family convey ideas of family through their self-produced YouTube videos? The results revealed that the ACE Family frequently and consistently uses various parasocial interaction strategies to convey images of normalcy while presenting non-traditional ideas of family similar to professionally produced programming. This paper contributes to understandings of how parasocial interaction strategies can be effectively used in portraying representations or images of social media influencers and how media portrayals of family are evolving with the affordances of user-generated content.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".