Bibliographic record
Abstract
How well do you know the local music scene in Vancouver? On this episode of Below the Radar, we sit down with Jarrett Martineau, a figure who is very well acquainted with the music scene both locally and abroad. On a local level, Jarrett works as the Music Planner for the City of Vancouver, where he works hard to support the Vancouver music scene and all aspects within that. He is also the host of Reclaimed, which is a weekly series on CBC Music that explores the many worlds of contemporary Indigenous music. In this conversation, we talk to Jarrett about how affordability affects available venues, the diverse array of music being created within the city, and the power of providing the platform of radio to early career musicians.\nTo learn more about CBC’s Reclaimed, you can visit their website here: www.cbc.ca/mediacentre/program/reclaimed. You can also learn more about the Culture|Shift Plan for the City of Vancouver here: vancouver.ca/parks-recreation-c…culture-shift.aspx.\nBack in October, we collaborated with Jarrett on an events at SFU Woodward’s called “Songs of the Land: Tracing Global Pathways in Indigenous Music”. You can listen to the audio for that talk here: Sfuw-community-engagement – Songs-of-the-land.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.073 |
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".