Reflections in the Fishbowl: the Changing Role of Law Librarians in the Mix of an Evolving Legal Profession
Bibliographic record
Abstract
This paper by Victoria Elizabeth Baranow is a reflective piece after co-moderating a session at the 2017 CALL Conference in Ottawa with Shaunna Mireau. The session was titled ‘Unconference Through the Fishbowl: The Changing Role of Law Librarians in the Mix of an Evolving Legal Profession.’ A play-by-play article on the session was written based on notes and recollections from the session and published in the TALL Quarterly, the journal by the Toronto Association of Law Libraries. 1 The article was used as the basis for this paper; which goes one step further in attempting to answer some of the big questions we are faced with each day while also questioning some of the assumptions and wider cultural forces at play in our law librarianship profession. **
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.051 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".