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Record W2111027597 · doi:10.1068/a35107

Unpacking and Repackaging Regional Diversity: Office-Building Trajectories in Canada

2003· article· en· W2111027597 on OpenAlexaboutno aff
Igal Charney

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2003
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
FundersGoddard Space Flight Center
KeywordsUnpackingDiversity (politics)Perspective (graphical)Economic geographyProcess (computing)Regional scienceSortingComputer scienceArchitectural engineeringData scienceGeographyPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Office-building cycles have been subject to extensive research during the past decade. Researchers have contemplated various aspects of the most recent cycle (1980s to early 1990s), yet the multiple spatial dimensions have received only modest consideration. Taking a longer perspective than the typical building cycle, the author considers the extended trajectories of office development in Canadian cities and regions; these development avenues are shaped by components originating at various geographical scales. The major purpose is to demonstrate how these components come about in particular settings. The process of identifying and sorting components is quite complex because they are inherently interactive. Trajectories should be interpreted as packages of general and particular arrangements. These arrangements reflect the interactions between components of different geographical scales, interactions that customize place-specific patterns.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0060.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.154
Teacher spread0.144 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2003
Admission routes1
Has abstractyes

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