Where to after COVID-19? Systems thinking for a human-centred approach to pandemics
Bibliographic record
Abstract
The COVID-19 pandemic was the biggest public health crisis that the world experienced on a global scale in recent history. It exposed systemic weaknesses and fragilities in health, economic, political, environmental and social systems (Haley, Paucar-Caceres, and Schlindwein, 2021 ). Since the early days of the crisis, governments around the world sought evidence-based management strategies, turning to science to inform decisions (Yu et al., 2021 ). Interventions took the form of ‘technical fixes’ (quarantines, social distancing, border closures, contact-tracing apps, etc.) and contributed to economic recession (Taylan, Alkabaa, and Yılmaz, 2022 ), the further straining of already fragile health systems (Arsenault et al., 2022 ) and the entrenchment of existing social inequalities (Sidik, 2022 ). Some countries acted swiftly and had some temporary success at early containment, thus minimising social disruption. Most countries, however, scrambled to implement measures that did not control adequately and proportionally the dynamics of the pandemic and failed to address holistically the social, ecological, and systemic aspects of the problem (Mormina, 2022 ), thus resulting in concurrent pandemic-related problems that fed off each other. The response to the COVID-19 crisis centred on the human-virus nexus without sufficiently considering the web of bio-psycho-social and ecological interrelations in which both humans and viruses are imbricated.
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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| 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".