MétaCan
Menu
Back to cohort
Record W3026848200 · doi:10.22364/ebci.01

Building Fences at Borders or Improving the Quality of Life for the Desperate?

2006· article· lv· W3026848200 on OpenAlexaboutno aff
Henry Hubert

Bibliographic record

Venuenot available
Typearticle
Languagelv
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
FundersEuropean CommissionCentral European UniversityNorthern Illinois UniversityPrinceton UniversityYale University
KeywordsAtlantaEconomies of agglomerationQuality (philosophy)Economic geographyRegional scienceGeographyLibrary scienceComputer scienceEconomic growthEconomicsMetropolitan areaArchaeology

Abstract

fetched live from OpenAlex

More than 25 % of the 2000 largest companies in the world locate their main offices in these ten cities, and more than 56% of companies are located in 82 cities.If we compare our list with P.J.Taylor list created in 2001 based on the advertisements in The Economist, we deduce that our list contains all cities from that list; however 42 of our top 82 were not present in Taylor's list.Among the top 9 cities there are more than 10 headquarters -Houston (22), Atlanta (15), Calgary (13), Dallas (12), Hamilton (12), San Jose (12), Athens (11), Birmingham (11), and Cincinnati (11).From these data we can anticipate that we have come up with more comprehensive list that P. J. Taylor has, as well as that in our list Asian cities dominate the top ten positions.One explanation for this can be that Asian cities gain their superiority in lower positions of rank, i.e. from 100 world largest companies 15 are located in New York, 7 in Tokyo, and 6 in London, but from 500 largest companies -40 in Tokyo, 26 in New York, and 23 in London.It thus denotes that the density of agglomeration decreases for lower positions of rank, so it is obvious that most of the largest companies tend to locate their headquarters in the few biggest cities. Agglomeration and Symbolic CapitalData show that attention and information diffusion is four times higher for cities where the agglomeration persists -85% of present attention for cities in agglomeration versus 15% for cities in non-agglomeration list.Similarly, information diffusion manifests the same result -81% of cities in agglomeration and 19% cities in non agglomeration.Table 1 Attention and information diffusion Cities in agglomeration Cities in non-agglomeration Attention 85% 15% Information diffusion 81% 19%In order to investigate relationship among agglomeration, information diffusion, and present attention, we perform Pearson correlation test.The result shows that all correlations are significant at 0.01 levels.It thus proves that there are linkages among information diffusion, present attention, and agglomeration.But the data also show that attention has more profound correlation with agglomeration than information diffusion with agglomeration.In order to develop our argumentation, we divide the cities according to centre -periphery notion by ranking the cities both by top 47 ranks of agglomeration and attention.If we find no or insignificant difference in the ranks, we group them as cities in centre, or, to put it in other words, in the core of activities.Similarly, the remaining cities are grouped into periphery, non-culture.Rank difference shows that smaller differences in ranks are for non-culture cities where their ranks difference varies from 0 to 10, but for centre cities the variation is 0-16, for periphery -0-28.Majority of cities in out-of-culture group are located in the nonagglomeration list, therefore they score equally worse in both lists.At this point we can conclude that the centre and non-culture cities are more coherent in their characteristics than the periphery cities.The explanation can be Globalization and its Effect on Communities and Identities 29 that centre and non-culture is a main pair of opposition for maintaining centre position and sustaining the culture itself, and that there are cities that are not mentioned in the articles at all. Table 2The sample of cities sign system groups of world economy

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.277
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicRegional Economics and Spatial AnalysisFrench-language works237,207