You Are Not Alone: Understanding Representation in Popular Media and Viewer Meaning Through Degrassi and its Fan Communities
Bibliographic record
Abstract
This thesis argues that Degrassi was a television production with values of authentic representation and to make its viewers feel less alone. The program pushed cultural boundaries to create meaning for its audience that connects with viewers many years after the program has finished airing. Representations matter for both the represented and the viewer, and this work shows how media can shape audiences and culture. This work employed a mixed-methods approach, with data collected from social media sites, the show’s text, and interviews with key informants, including Degrassi’s co-creator and executive producer Linda Schuyler. This provided rich data about the history of Degrassi and its messaging with its viewers using emerging technology, elements that created meaning for viewers, and what the Degrassi community had to say about the show’s representations. With this thesis, I found that Degrassi producers embraced emerging technology at significant times in the show’s production to engage with viewers to distribute the show’s message. I found that viewers saw meaning in the show’s vision and values, its actors, characters, and stories, and its Canadian essence. When considering the representations on Degrassi, there was a consistent pattern that showed that its portrayals made viewers feel less alone, provided opportunities for empathy towards unfamiliar groups, and created avenues for people to see others like them on screen. These representations were said to be ahead of their time and opened up opportunities for similar works within the cultural climate.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".