Bibliographic record
Abstract
The current COVID-19 crisis emphasizes that pandemics are both biomedical phenomena and significant multifaceted historical events. The 1918–19 Influenza Pandemic, known as the “Spanish Flu,” was estimated to have killed between fifty to one hundred million people, yet historians like Alfred Crosby labelled the Spanish Flu a “forgotten” pandemic because of its absence from contemporary and academic writing. Memory of the pandemic has recently received academic attention in Pandemic Re-Awakenings (2022) though memories of thepandemic in Newfoundland and Labrador (NL) were not explored in that work. This paper examines the memory of Spanish Flu in NL using both “global” and “local” perspectives and argues for the importance of applying globally recognized academic theory, methodology, and examples to local perspectives and experiences. During an era fixated on remembrance, the pandemic dead were concealed by the Great War, except members of the community who fit into heroic narratives. Without utilitarian value, the memory of the Spanish Flu was “forgotten,”an active and passive process that downplayed its impact on human society. Memory of the Spanish Flu persisted in private form and influenced the world through public health policy changes and as a precedent-setting event to be viewed in grim anticipation of future pandemics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".