Bibliographic record
Abstract
Information from the Dictionary of Canadian Biography was selectively augmented from other resources including Wikidata and scholarly sources to provide fundamental information about individuals and their families, such as marital status, occupation, residence, and gender. Unnamed individuals are included with labels such as “A Free Black mother.” Place names are geolocated. A small selection of links to digitized Canadian census records and fonds published by Library and Archives Canada are included. The data includes links to the sections of the Dictionary of Canadian Biography entries from which the set of individuals covered in the datasets and the primary assertions about them were derived. Although the team devised a preliminary model for nationality aligned with other LINCS data, in the end designations related to individuals’ nationality, national origin and ethnicity were not carried over from the source. The team lacked decolonized vocabularies against which to map terms for First Nations, Inuit and Métis people, and was wary both of perpetuating colonial terminology and of omitting such designations solely for Indigenous people. Creators and Contributors Jim Clifford, Susan Brown, Sarah Roger, Natalie Hervieux, Matthew Kunkel, Jakob McLellan, Zach Shoenberger, Thomas Smith, Jessica Ye, Dani Metilli, Linked Infrastructure for Networked Cultural Scholarship (LINCS) Support The Historical Canadian Persons dataset was made possible thanks to the generous support of Library and Archives Canada, the Canada Foundation for Innovation, and the Social Sciences and Humanities Research Council of Canada Forward Linking Partnership Development Grant.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.033 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.097 |
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".