Bibliographic record
Abstract
The social and ecological changes accompanying the Anthropocene require changes in how pandemics are anticipated, conceived, and managed. Pandemics need to be reframed from infections we can predict to inevitable infectious and non-communicable surprises with which we need to cope. A hazard-by-hazard approach to planning and response is insufficient when the next pandemic cannot be predicted. Decision-making will benefit from scoping the problem broadly to generate deeper insights into potential threats. The origins of pandemics come from our relationships with the world around us. Health leaders, therefore, need to be aware of primordial determinants of risk arising from these changing relationships. Cross-sectoral co-learning to anticipate surprise will require bridging agents embedded within a health agency to facilitate transdisciplinary intelligence gathering. A unified set of guidelines is needed to promote pandemic resilience by collaboratively tending to the determinants of health for each other, our communities, and the natural environment.
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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".