Bibliographic record
Abstract
The articles published in our Winter 2016 edition are connected loosely under the themes of pop culture and public memory. Our annual conference, held in late April, featured Dr. Kelly MacFarlane as our keynote lecture to speak on the interplay between history, public memory, and pop culture. We are thrilled to present to you eight excellent articles in our Winter 2016 edition: An ideological examination of the US-Russian Space Race and public memory of the event during the cold war is offered in "Narrative Memory in the Space Race"; The article "The Swastika and the Maple Leaf: National-socialism and anti-semitism in Canada" returns to the 1930s to explore anti-semitist ideologies in our country preceeding the second world war;"The Monumentalization of our Disgrace" is a captivating look at the manner in which concentration and death camps standing after the Holocaust are memorialized today and the local residents' public perception of such memorials; "Hegemony without Tears: Defenitions and uses of hegemony from Gramsci onwards" explores the works of Marxist writer Antonio Gramsci and his influence upon other writers through several case studies, ultimately commenting on the subsequent reinterpretation of Gramsci's definition of hegemony; "Confrontation and Cooperation: the hidden history of national parks and Indigenous groups in Canada" takes the reader through a historical analysis of Canada's national parks and the indigenous groups originally inhabiting them, arguing the erasure of these groups by the National Parks system; "War and Faith: Memories of the Great Patriotic War in the Russian Orthodox church" examines the Church's modern conceptualization of the Soviet Union as a "holy battle for survival; "Administering State Legislation: The kirk and witchcraft in early modern Scotland" is a fascinating look at the relationship between the duties performed by the Royal Court and the Calivinist church in Scotland; and finally, we have "The Limits of Rationalism: Early modern geography and the idea of Europe", an investigation of cartogropher's contributions in the 16th and 17th century towards the concept of 'Europe' and the growth of a scientific worldview. We wholly hope you enjoy our Winter 2016 edition as much as our staff has enjoyed curating it. Editors Emily Kaliel Ellen Sutherland Assistant Editors Jean Middleton Kayla Pituka Senior Reviewers Kyler Chittick Emily Hoven Katarina Hoven Faculty Advisor Jeremy Caradonna
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.166 | 0.119 |
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".