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Record W3151038965

The New Oxford Handbook of Economic Geography

2017· article· en· W3151038965 on OpenAlexaboutno aff
Dariusz Wójcik

Bibliographic record

VenueOUP Catalogue · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFinancializationFinancial crisisGeography of financeEquity (law)Evolutionary economicsMainstreamEconomicsSustainabilityEconomic stabilityEconomyFinancePolitical scienceNeoclassical economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The first fifteen years of the 21st century have thrown into sharp relief the challenges of growth, equity, stability, and sustainability facing the world economy. In addition, they have exposed the inadequacies of mainstream economics in providing answers to these challenges. This volume gathers over 50 leading scholars from around the world to offer a forward-looking perspective of economic geography to understanding the various building blocks, relationships, and trajectories in the world economy. The perspective is at the same time grounded in theory and in the experiences of particular places. Reviewing state-of-the-art of economic geography, setting agendas, and with illustrations and empirical evidence from all over the world, the book should be an essential reference for students, researchers, as well as strategists and policy makers. Building on the success of the first edition, this volume offers a radically revised, updated, and broader approach to economic geography. With the backdrop of the global financial crisis, finance is investigated in chapters on financial stability, financial innovation, global financial networks, the global map of savings and investments, and financialization. Environmental challenges are addressed in chapters on resource economies, vulnerability of regions to climate change, carbon markets, and energy transitions. Distribution and consumption feature alongside more established topics on the firm, innovation, and work. The handbook also captures the theoretical and conceptual innovations of the last fifteen years, including evolutionary economic geography and the global production networks approach. Addressing the dangers of inequality, instability, and environmental crisis head-on, the volume concludes with strategies for growth and new ways of envisioning the spatiality of economy for the future. Contributors to this volume - Philip Auerswald, George Mason University. Harald Bathelt, University of Toronto. Michael Berry, RMIT University, Australia. Ron Boschma, Utrecht University. Kam Wing Chan, University of Washington. Karen Chapple, University of California, Berkeley. Susan Christopherson, Cornell University. Gordon L. Clark, University of Oxford. Jennifer Clark, Georgia Institute of Technology. Neil M. Coe, National University of Singapore. Stuart Corbridge, Durham University. Lokesh Dani, George Mason University. Mercedes Delgado, MIT Sloan School of Management. Danny Dorling, University of Oxford. Gilles Duranton, University of Pennsylvania. Gary A. Dymski, University of Leeds. Benno Engels, RMIT University, Australia. Maryann P. Feldman, University of North Carolina. Richard Florida, University of Toronto. Chris Forman, Cornell University. Koen Frenken, Utrecht University. Meric S. Gertler, University of Toronto. Amy Glasmeier, Massachusetts Institute of Technology. Johannes Gluckler, Heidelberg University. Avi Goldfarb, University of Toronto. Gernot Grabher, HafenCity University Hamburg. Shane Greenstein, Harvard Business School. Dieter Helm, University of Oxford. Cameron Hepburn, University of Oxford. Alex Hughes, Newcastle University. Simona Iammarino, London School of Economics and Political Science. Oliver Ibert, Freie Universitat Berlin. Natasha Iskander, New York University. Chacko G. Kannothra, University of Massachusetts Boston. William R. Kerr, Harvard Business School. Janelle Knox-Hayes, Massachusetts Institute of Technology. Karen P.Y. Lai, National University of Singapore. Marcus M. Larsen, BI Norwegian Business School. Robin Leichenko, Rutgers University. Mark Lorenzen, Copenhagen Business School. Nichola Lowe, University of North Carolina at Chapel Hill. Stephan Manning, University of Massachusetts Boston. Ron Martin, University of Cambridge. Philip McCann, University of Groningen. Caitlin A. McElroy, University of Oxford. Anita M. McGahan, University of Toronto. Sarah McGill, University of Oxford. Charlotta Mellander, Jonkoping International Business School. Ashby Monk, University of Oxford. Phillip O'Neill, Western Sydney University. Jamie Peck, University of British Columbia. Alexander Pfeiffer, University of Oxford. Andres Rodriguez-Pose, London School of Economics. Rajiv Sharma, University of Oxford. Eric Sheppard, University of California, Los Angeles. Janice Stein, University of Toronto. Michael Storper, London School of Economics. Kendra Strauss, Simon Fraser University. Alexander Teytelboym, University of Oxford. Maria Tsampra, University of Patras, Greece. Callum Wilkie, London School of Economics. Neil Wrigley, University of Southampton. Henry Wai-chung Yeung, National University of Singapore. Dariusz Wojcik, University of Oxford. Matthew Zook, University of Kentucky.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0020.003
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0490.020

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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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