Meanwhile at Concordia University …
Bibliographic record
Abstract
The Department of History at Concordia University in Montreal\nhas been an especially active place in recent months. Our\nfaculty members’ achievements include the Nanovic Institute’s\n2019 Lara Shannon Prize in Contemporary European Studies,\nawarded to Max Bergholz for Violence as a Generative Force:\nIdentity, Nationalism, and Memory in a Balkan Community\n(Cornell UP, 2016). This is the fifth major prize that Max has\nwon for this insightful monograph. Meanwhile, Sarah Ghabrial\nhas been offered a visiting fellowship at the Shelby Cullom Davis\nCenter for Historical Studies at Princeton University, which she\nwill take up in the Winter 2020 term. Norman Ingram has just\npublished (February 2019) a new monograph with Oxford University\nPress entitled The War Guilt Problem and the Ligue des\ndroits de l’homme, 1914-1944. And in July 2019, Anya Zilberstein\nwill once again be offering Edible Environments: In and\nBeyond Montreal as part of Concordia’s International Graduate\nSummer and Field Schools. Just as exciting is the news that the\nDepartment, in partnership with the Centre for Oral History\nand Digital Storytelling (COHDS) and the First Peoples Studies\nprogram, has just been authorized to search for a Tier-II Canada\nResearch Chair in Indigenous Oral Tradition and Oral\nHistory.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.370 | 0.130 |
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".