Bibliographic record
Abstract
Thinking about television's relationship with place began when I was six, singing about measurement, currency, and geography in segments for the Canadian version of Sesame Street that my dad, Clive VanderBurgh, created for the Canadian Broadcasting Corporation in Toronto.Since then, my lived experience as a child of a talented and prolific tv producer during Canada's culture-funding heyday, and later, as an entry-level worker in the industry, showed me how governmental, institutional, and contractual policies shape what kind of television gets produced, how it's likely to circulate, and whether (or not) it remains accessible over time.The best aspects of this book are due to the incredible teachers and mentors that I've had at every stage of my education and career.I'd like to particularly acknowledge my elementary music teacher, John Collette, who, among his many wonderful aspects, passionately championed the arts in a place where it was needed, never forgot the trombone players, and modelled a dedication that continues to inspire me.Anne Schmidt, one of my wonderful high-school English teachers, showed me how to translate a love of other people's creative work into disciplined, analytical practice.She taught us a valuable lesson that good critical writing is an art in itself.I owe a lot to my first, informal research colleagues, with whom I spent many late nights in suburban basements, watching videotaped recordings of television, over and over (and over) again.While my viewing goals are different today than learning the choreography to Michael Jackson's "Thriller
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.477 | 0.308 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".