Let Me Draw You A Map: Knowledge Management From “Two Completely Different Streams of Thought”
Bibliographic record
Abstract
This paper represents the results of a conversation between Adrienne Heavy Head, the creator and manager of the Blackfoot Digital Library (BDL), at the University of Lethbridge in Alberta, Canada, and Mary Greenshields, a new librarian in Alberta. The aim of the conversation was for Mary, a settler living in traditional Blackfoot Territory, to learn about the creation and maintenance of the BDL, and to gain insight into the organization, access, and classification of information within the library as a real life example of some of the protocols suggested by the Canadian Federation of Library Associations (CFLA). Adrienne and Mary hope that this conversation will help librarians to better understand knowledge management from a Blackfoot perspective and might inspire librarians to start such conversation with the Indigenous peoples upon whose land their libraries rest.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.035 | 0.041 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".