Bibliographic record
Abstract
Please welcome as guest author our long-standing colleague and friend Colleen Beard, Librarian Emeritus, Map, Data and GIS, Brock University. Colleen’s account of her ongoing research opens our eyes to the fascinating environmental history of the Welland Canal. She shares her experience of involvement with a SSHRC Insight Development Grant-funded project, and explains how knowledge of local environmental history and the varied technical skills of our trade have much to add to academic partnerships. As we redefine the nature of our work and reconsider research library mandates, the trend towards becoming full partners in research is important - a wonderful challenge and opportunity for many of us. Thank you, Colleen for continuing to inspire and lead us! We’ll see you at Puddy’s Bar & Grill for the next HWCMP talk! Barbara Znamirowski (Editor, GIS Trends)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.048 |
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".