TechBC Memory Project: Tim Rahilly
Bibliographic record
Abstract
Tim Rahilly is the subject of this interview. He started at TechBC in 1999 as Director, Learner Services. He left TechBC in December 2001. He returned to SFU in 2003 and is now Associate Vice President, Students. The interview took place on 11 December 2014 at SFU Burnaby.\n \nTim Rahilly discusses the formational, functional and terminal phases of TechBC from an administrative perspective. He describes his initial role at the school processing admissions, and how that role soon expanded in such a small institution. He discusses the unique location and culture of TechBC, and the close relationship amongst staff, as well as between staff and students. This close relationship would eventually lead to Rahilly’s resignation shortly before the announcement of the SFU take-over, as he felt that the students were suffering under rumours of the school’s dissolution. Rahilly characterizes himself and other staff as having been naively overly-dedicated to the cause of the school, but far from being rueful, he reflects on the successes and failures of TechBC, and what it was like being part of SFU after they assumed control.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.296 | 0.104 |
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".