The COVID-19 pandemic: territorial, political and governance dimensions of the crisis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As editors of Territory, Politics, Governance, we want first and foremost to express our solidarity with those affected and impacted directly by the COVID-19 pandemic. While none of us is untouched by the current public health crisis, what has unfolded thus far reveals only too clearly the inbuilt inequalities of contemporary capitalist society in terms of mortality, illness and recovery (for a pre-COVID-19 discussion of the United States, see Case & Deaton and for the UK, see Wilkinson & Pickett). In the UK and United States, for example, ethnic minority communities are overrepresented in terms of mortality from COVID-19 (The Guardian). Key workers (and ethnic minority communities are overrepresented in some areas such as health and social care) continue to operate in circumstances (not of their own choosing) where, depending on country and locale, the availability of personal protection equipment (PPE) is widely different in terms of efficacy, quality and protection standards. Crises often reveal what Shuster describes as structural inequalities (such as the unequal distribution of resources or the uneven delivery of healthcare) that produce harmful effects against some groups more than others. The UK Office of National Statistics (ONS) released March–April 2020 data for England and Wales which revealed that COVID-19-related death rates in the most deprived areas are more than double those of the less deprived. Profound socioeconomic-, gender-, class- and ethnicity-related disparities in COVID-19 mortality are being revealed on a weekly basis (ONS).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it