In too deep when Canadian punks took over the world
Bibliographic record
Abstract
"The unlikely story of a bunch of small-town Canadian punks who conquered the global music industry. After punk found commercial success in the '90s, with bands like Green Day, the Offspring, and Blink-182, a new wave of punk bands emerged, each embodying the DIY spirit of the movement in their own way. While Southern California remained the spiritual home of punk rock in the early 2000s, an unexpected influx of eager punks from Canada took the world by storm, changing the genre forever. Drawing on exclusive interviews and personal stories from nine artists of the era, In Too Deep explores how Canada became the improbable birthplace of a new age of punk icons. Covering the rowdy punk rock of Gob and Sum 41, the arena-sized ambitions of Simple Plan and Marianas Trench, the reinvention of the popstar by Avril Lavigne and Fefe Dobson, and the quest to bring hardcore into the mainstream by Billy Talent, Silverstein, and Alexisonfire, In Too Deep traces the evolution of a music scene that challenged notions of who and what should be considered punk while helping to define Millennial culture as some of their generation's first superstars."
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 | 0.000 | 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 | 0.038 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".