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Record W4378832554 · doi:10.1177/20438206231178818

Economic geography for and by whom? Rethinking expertise and accountability

2023· article· en· W4378832554 on OpenAlex
Emily Rosenman, Priti Narayan

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDialogues in Human Geography · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAccountabilityPandemicPublicsSociologyIdentity (music)Field (mathematics)Political economyPolitical scienceCoronavirus disease 2019 (COVID-19)EconomyEconomicsLawAestheticsMedicine

Abstract

fetched live from OpenAlex

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?

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.252
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it