Lynda Roberts, Manager of Library Services
Bibliographic record
Abstract
In this presentation I will provide practical examples of some of the Knowledge Management resources used at Bull Housser & Tupper. We adopted Knowledge Management a couple of years ago, and, despite only a modest financial investment in the strategy, have developed several key resources. Before I present the resources I will describe, briefly, how we came to know and love KM @ BHT. As a librarian I see, as my biggest challenge, the need to organize, index and co-ordinate everything! As an administrative manager in a law firm I see, as my biggest challenge, the need to justify an ever increasing salary, without ever having to meet a billing target! It seems obvious that to meet both challenges I should focus on no less than: organizing all the operations and documents in the firm; arranging for and organizing access to the necessary external resources; and turning all of this into specialized resources that assist the lawyers work more efficiently making them look good in the eyes of our clients while adding value to my position. This was our first KM strategy! Two years later, Knowledge Management, while still not defined in a formal strategic document, is alive and well at Bull Housser and Tupper. Located in Vancouver, the firm has about 100
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.366 | 0.264 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".