Bibliographic record
Abstract
Tell us about your current position I’ve been in my current role only a few months now, so I’m still learning about it! The Centre for Addiction and Mental Health (CAMH) and its library is a fascinating place to work. There’s much that you would find typical in a library for an academic teaching hospital, but there’s much that’s unique as well. Focussing on a patient population that has mental health and addiction issues is a real eye-opener. I find it’s totally changing my perspective on much of news and even pop culture once your awareness has been raised around issues of stigma, language, trauma, etc., you can’t help but place a different lens over everything that you look at. It’s been a while since I’ve worked in an actual library, so it’s fun getting re-acquainted with that world. There’s the more mundane we just completed our annual subscription cancellation, er, I mean renewal, process. And then there’s the truly exciting we’ve just kicked off planning for brand new space in a building that is yet to be built. We’ll spend three years planning and then three years building, and if all goes according to plan, we’ll be moving into a brand new space at CAMH’s Queen Street West campus in downtown Toronto in 2020. The new space should be amazing we’ll be sharing ground floor space right on Queen St. West with the new student centre, conference centre, and Workman Arts theatre. There really is a transformation happening all across CAMH. It’s impacting everything from the physical spaces to clinical practice. I think the next decade here is going to be a blast! Your previous positions have been quite information technology (IT) intensive. What made you decide to move into a new role? How does your previous IT experience inform your work as the Director of Library Services at CAMH? The last 13 years I’ve been in a ‘‘nontraditional’’ role, that’s true, but I’ve always thought I’d return to the library world at some point. I’ve always loved the field, which is why I maintained my connections, primarily through my involvement in CHLA, with health librarians across the country. It’s not often an opportunity like this comes along, so I jumped at the chance. I think having IT experience is a definite benefit. So much of what we do is dependent upon technology that it really streamlines things when your IT folk know that you understand their language and their constraints. Having that background allows me to participate in other projects that are going on within Education at CAMH. There are a number of technology heavy projects around portal development and mobile app development that I’m just starting to get involved with, but I think the variety of work that I’m going to be getting involved with is incredible.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.639 | 0.461 |
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".