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Record W2520361930 · doi:10.1177/1470593116657915

Introducing a spatial perspective to analyze market dynamics

2016· article· en· W2520361930 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

VenueMarketing Theory · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsYork UniversityConcordia University
Fundersnot available
KeywordsUnpackingPerspective (graphical)Dynamics (music)Space (punctuation)Economic geographySociologyThrough-the-lens meteringWork (physics)Computer scienceLens (geology)EconomicsEngineering

Abstract

fetched live from OpenAlex

Grounded in work on geography and markets, this article offers a conceptual framework to study the dynamics of markets through a spatial lens. The characteristics of four key spatial dimensions (place, territory, scale, and network) are explained and leveraged to provide distinct analytical vantage points and to conceptualize how various types of spaces matter differently in market dynamics. Findings from a qualitative meta-analysis identify 12 unique mechanisms tied to the four proposed spatial dimensions, which offer alternative theoretical avenues for unpacking market phenomena. These four spatial dimensions are then combined with 12 space-based mechanisms to offer novel research avenues for marketing scholars interested in market system dynamics.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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