Crossroades : Historians ans Scholarship for an uncertain world
Bibliographic record
Abstract
Robocalls, repression in Syria, contested elections in Myanmar and Russia, environmental issues like climate change and radioactive fallout from the Fukushima reactor, and the ongoing economic crisis in Europe are all contemporary events with deep historical roots.When we meet for the 2012 Canadian Historical Association annual meeting from May 28 to 30 this spring, historians and their colleagues from other societies and associations will be presenting papers which analyze the past and demonstrate its profound impact on the present.We will also be discussing the many ways which our scholarship contributes to current discourse and will debate future directions for our discipline in light of digitization in research and teaching. The broad-ranging program for our annual meeting reflects the many varieties of history and scholarship which characterize our discipline and will be a wonderful opportunity to share new insights, analytical tools and techniques and to meet old friends and make new ones.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.059 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 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".