But where's the body? Bodies, time, money, and the political economy of post-pandemic field research
Bibliographic record
Abstract
Since the pandemic, field work has been transformed by shifts in the political economy affecting the material conditions underpinning research. In this research note, a research team considers their challenges and learning in completing field studies conducted in 2022, including intensified strains on time, money, researchers' bodies, and risks associated with illness and infection spread. We argue that a neoliberal "research super-hero" norm operates within the research community, rooted in a conception of high productivity that mingles uneasily, for many researchers, with feminist, anti-racist, and anti-colonial social justice aims and responsibilities. Our 2022 fieldwork experience led us to notice how this norm has circulated within our explicitly feminist research team and nudged us to challenge it, while raising questions about how a "research-worker" norm can best be supported.
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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 | 0.105 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.112 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".