Bibliographic record
Abstract
Andrew Petter is the subject of this interview. He held a number of cabinet positions in the NDP government that created TechBC, including responsibilities for Crown corporations and Advanced Education. He is now President of Simon Fraser University.\n \nAndrew Petter discusses the relationship between the provincial government and the organizers of TechBC, specifically regarding accountability, return on investment, and especially the location of the school. He relates the part played by himself, former chair of ICBC Bob Williams, and architect Bing Thom in establishing central Surrey as the eventual site for TechBC. This he attributes to Bing Thom’s vision of revitalizing the Whalley area, as well as strategically connecting the school to downtown Vancouver via the skytrain. Ultimately, the decision to locate the school closer to Vancouver rather than further out in the Fraser Valley would affect the decision to make the curriculum technical, rather than trade-oriented. Petter concludes that the eventual take-over of TechBC by SFU was ultimately beneficial: while the component decisions may in retrospect seem to have been wrong, the final result was an excellent research university in the right location.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.168 | 0.031 |
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".