Community Engagement at Western University Archives: Celebrating Our Relationships with Communities, Collections and Donors
Bibliographic record
Abstract
This panel presentation focuses on innovative ways that Western Archives is increasing its level of engagement with various communities, such as students, faculty, alumni, local researchers, and donors. Examples include a Canada 150 project to digitize and publish thousands of London Free Press photographs of the centennial year 1967 and the creation of a virtual exhibit celebrating the Labatt Brewing Company’s 170th anniversary. We used social media to promote and share content from both the London Free Press and Labatt exhibits. Beyond these specific initiatives, Western Archives also engages with various memory and heritage Facebook groups to promote our holdings. We will share some initial impressions and data related to the measurement of success of our initiatives. We discuss the challenges of community engagement such as ensuring proper attribution, as well as balancing this work with the more traditional collection management activities.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".