The Indigitization Tool Kit for First Nations Community Digitization Projects
Bibliographic record
Abstract
Webcast sponsored by the Irving K. Barber Learning Centre and was part of the Aboriginal Unhistory Month month-long series of events at UBC.The Indigitization Tool Kit is a how-to resource for First Nations communities digitizing cultural materials, such as open reel audio tapes from oral histories. Special guest Khelsilem will also speak about his involvement in the project. Presenters include Mimi Lam (UBC Librarian, Digital Projects), Gerry Lawson (Oral History Lab Coordinator, Audrey & Harry Hawthorn Library & Archives at MOA), and special guest Khelsilem (formerly Dustin Rivers), a Squamish/ Kwakwaka’wakw student, cultural educator and language enthusiast. This event is part of the Aboriginal (Un)History Month events, coordinated by UBC Library, in partnership with the Musqueam Indian Band, the Centre for Teaching and Learning Technology and the Museum of Anthropology. This event took place at the Dodson Room (302), Irving K. Barber Learning Centre, 1961 East Mall, University of British Columbia, on June 25, 2012.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.070 |
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".