When First We Practice to Deceive: The Semiotics of the Chinese TV Drama The First Half of My Life
Bibliographic record
Abstract
Abstract In the darkest days of the pandemic, an online streaming service offered escape in the form of a 42-episode Chinese dramatic TV series, The First Half of My Life (2017). This paper provides a history of semiotic thought followed by an analysis of a woman’s professional life in the Peoples Republic of China. It uses, Canadian Sociologist Irving Goffman’s concept of dramaturgy and Austrian social psychologist Fritz Heider’s balance theory. This popular series is the story of the paradigmatic transformation of its female heroine, Luo Zijun, from dependent housewife to independent businessperson. Her ex-husband declares, “I never imagined she’d become like a different person” (Episode 14, 2017). She has help from her family, friends, and mentors for a syntagmatic change. The pain, loneliness, and courage are all personal as she sheds negative habits and builds positive responses to family and work challenges. Zijun’s relationships become more complex as she balances her new responsibilities as a professional and a mother. This paper pays attention to issues like the struggle between individualism and collectivism in modern China. It demonstrates how women deal with discrimination in divorce proceedings and stigma in the workplace. It brings the privileges and problems of millennials into clear focus. One reviewer succinctly summed up the show, “The romance is almost nonexistent, and it is more of a slice of life of 30-something year old characters growing as they navigate through what life throws at them” (My drama list – reviews, 2017. p. 1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".