TechBC Memory Project: Tracey Leacock
Bibliographic record
Abstract
Tracey Leacock is the subject of this interview. At TechBC, she was an Associate Dean (Academic Projects) and Assistant Professor. The interview took place on 8 December 2014 at SFU Vancouver.\n \nTracey Leacock discusses her role in working with fellow TechBC faculty to develop curriculum, specifically the online component of classwork. She characterizes this process as being time-consuming (cutting into potential research time for faculty) but ultimately rewarding. Online learning, she says, involved far more discussion at TechBC than it does at other institutions today, but it was also less public and practical. Leacock says the advantage TechBC had as a start-up was that they could develop truly interdisciplinary learning, rather than having sequestered departments as in more established schools. Emphasis on curriculum development rather than research was inevitable at a start-up, says Leacock, as was the high cost per student, therefore, in her opinion, the decision to close down TechBC was unjustified: the experiment should have been given more time. Leacock compares the unique culture of TechBC to a family with all its associated angst, as well as the willingness of each member to do their bit to contribute to the whole. That culture, however, as well as TechBC's progressive pedagogy, was essentially killed with the transition to SFU.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.119 | 0.022 |
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".