Bibliographic record
Abstract
Abstract: In this article we explore what the exploding world of humanities and social science research infrastructures might mean for teaching and research in the discipline of history. We focus closely on one example, that of the Canadian Century Research Infrastructure Project (ccri). This interdisciplinary and multi-university project has constructed an infrastructure composed of microdata from the nominal-level Canadian censuses from 1911 through 1951. In addition to compiling information on approximately 2 million individuals, the ccri created a database of contextual data and a gis database. The combination of these three levels makes this infrastructure unique in the world. The ccri can be used in conjunction with Canadian census databases now being constructed or already completed for Canada from 1851 to 2001. As well, the ccri has been constructed in ways that will facilitate cross-national explorations with the United States, the United Kingdom, and several other North Atlantic countries. We suggest that the ccri can best be appreciated when situated within the current proliferation of research infrastructures across the humanities and the social sciences. We argue that these infrastructures are liberating for historians and, collectively, represent new horizons for professional activity. It would be a disservice to themselves, their students, and their profession if historians ignored these expanding horizons.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.798 | 0.580 |
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".