Economic geography for and by whom? Rethinking expertise and accountability
Bibliographic record
Abstract
This commentary builds on Doreen Massey's thinking on the economy and relationality to ask: who gets to produce economic knowledge and whose lives does research make visible as economic matters of concern? These questions have been thrown into sharp relief as a result of the COVID-19 pandemic. While the pandemic has highlighted the need for better infrastructures of care, it has also demonstrated that the mission of ‘saving the economy’ from the ravages of COVID-19 has not centred the concerns of those who have experienced the crisis most acutely. Drawing inspiration from the various economic subjects who continue to make, re-make, and articulate the economy through regular shocks and crises – workers, caregivers, and people marginalized by identity or geography – this commentary makes a case for a public economic geography that rethinks who is taken seriously as an ‘expert’ on the economy, and to what publics the field speaks. This, at its heart, is a radical rethinking of accountability, calling on economic geographers to ask: what should research do for whom, and how?
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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 | 0.086 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.114 |
| Scholarly communication | 0.028 | 0.048 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".