Cancer, COVID-19, and the need for critique
Bibliographic record
Abstract
In this open letter we examine the implications of the coronavirus disease 2019 (COVID-19) pandemic for cancer research and care from the point of view of the social studies of science, technology, and medicine. We discuss how the pandemic has disrupted several aspects of cancer care, underscoring the fragmentation of institutional arrangements, the malleable priorities in cancer research, and the changing promises of therapeutic innovation. We argue for the critical relevance of qualitative social sciences in cancer research during the pandemic despite the difficulties of immersive kinds of fieldwork. Social science research can help understand the ongoing, situated and lived impact of the pandemic, as well as fully underline its socially stratified consequences. We outline the risk that limiting and prioritising research activities according to their immediate clinical outcomes might have in the relational and longitudinal understanding of cancer practices in the UK. Finally, we alert against potential distortions that a “covidization” of cancer research might entail, arguing for the need to maintain a critical point of view on the pandemic.
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 | 0.106 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".