Bibliographic record
Abstract
In 2007, the CW network debuted an hour long teen drama entitled Gossip Girl. The program was based on the already popular novels of the same title by Cecily von Ziegesar (The CW, 2008). While this teen drama resembles the many teen dramas before it, there is a distinctive production element which makes this program stand out, the costuming. The styling of the program is the creation of Eric Damam, who ironically also was the stylist for the fashion forward program “Sex in the City” (Wharmby, 2008). In any given episode designer names such as Christian Louboutin, Tory Burch, or Chanel can be heard and seen on the various characters. When not in top designer names, the character’s costuming still present an unseen level of glamour and innovation. With so much focus placed on the wardrobe of the characters, it must be questioned what this translates to for the audience. This research will set out to identify whether the costuming of the Gossip Girl characters affects the audience’s personal fashion choice. In order to discover the answer to this question, three steps are taken. Firstly, a brief literature review will explore the scholarly work published in regards to television show’s affects on viewer behaviour, specifically on fashion choices. Secondly, a small content analysis will attempt to establish how important costuming is to the production of Gossip Girl. Thirdly, an audience study will give insight into how this importance of costuming affects viewers’ personal fashion choices.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".