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Record W1966473393 · doi:10.1080/08865655.2001.9695576

Regional identity in border regions: The difference borders make

2001· article· en· W1966473393 on OpenAlexvenueno aff
Michael Schack

Bibliographic record

VenueJournal of Borderlands Studies · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsClosenessTerritorialityGermanPerceptionDanishContext (archaeology)Economic geographyIdentity (music)State (computer science)Political sciencePoliticsGeographyRegional scienceSociologyPsychologyLawCommunication

Abstract

fetched live from OpenAlex

One significant question for border studies is whether distance translates into closeness. That is, does proximity to state‐borders and the neighboring state constitute an interaction context in and of itself? This article relies on data from a research project that was conducted to investigate the perception of neighboring country high school students inside and outside the Danish‐German border region. The results of the research points towards an understanding of border regions as regions where several dimensions of social interaction play an important role. Although the research results provide evidence for the importance that nation‐states play in perceptual differences between borderlands and non‐borderlands, it is not the case on both sides of the Danish‐German border. Therefore, contexts other than nation‐state borders must be considered. This article argues that the perception of the neighboring country and the border region rely on specific types of cross‐border activities and associations with the neighboring country. The less important state‐borders become as markers of territoriality and control, the more other types of boundaries might become visible.

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)0.002
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0010.001
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 head0.062
GPT teacher head0.430
Teacher spread0.368 · 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

Citations20
Published2001
Admission routes1
Has abstractyes

Explore more

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