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Relational Research Design in Economic Geography

2018· book· en· W2792544840 on OpenAlex

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

VenueOxford University Press eBooks · 2018
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelational theoryContingencyPerspective (graphical)Relational viewDisciplineInterdependenceSpace (punctuation)SociologyKnowledge managementStrategic geographyAction (physics)Economic geographyEpistemologyManagement scienceSocial scienceGeographyHuman geographyComputer scienceEngineeringHistorical geographyArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter discusses the nature of relational research designs that aim to overcome separations between different disciplinary perspectives within economic geography and create linkages to other academic fields. The relational approach is a comprehensive research perspective grounded in three principles of relationality of economic action: contextuality, path dependence, and contingency. Using the cases of manufacturing versus professional services clusters, it is shown that the relational approach does not proclaim a meta-theory of economic organization in space but provides a framework for contextual theorization, adjusted to the specific sectoral and technological contexts under investigation. Relational research designs across academic fields agree (i) that social relations between people and organizations are key to understanding the contemporary economy, (ii) that economic processes rest on the spatial and temporal interplay between regional and global networks, and (iii) that innovation and learning depend on simultaneous inter-firm, intra-organizational and community-based interactions and relations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.243
Teacher spread0.146 · 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