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Record W2119195201 · doi:10.5194/sg-4-47-2009

Critical geography in Germany: from exclusion to inclusion via internationalisation

2009· article· en· W2119195201 on OpenAlexaff
Bernd Belina, Ulrich Best, Matthias Naumann

Bibliographic record

VenueSocial geography · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsGermanInternationalizationCritical geographyHuman geographyGeographyInclusion (mineral)Economic geographySightInclusion–exclusion principleCultural geographyPolitical scienceRegional scienceSocial scienceSociologyArchaeologyLaw

Abstract

fetched live from OpenAlex

Critical perspectives have become more visible in German human geography.Drawing on an analysis of the debate around the German reader "Kulturgeographie" published in 2003, we suggest that this case provides new insights into the "geography of critical geography".We briefly discuss the history of critical geography in Germany, leading to a comparison of the conditions of critical geography around 1980 and in recent years.The focus is on two factors in the changed role of critical perspectives in German geography: (1) the growing internationalisation of German geography, which opened new avenues and allowed new approaches to enter the discipline; and (2) the high citation indices of "critical" journals, which leads to an enhanced reputation and a high significance of international critical geography in the German discipline.However, we draw an ambiguous conclusion: the increased role of critical approaches in German geography is linked to a growing neoliberalisation of academia and a decline of critical approaches in other disciplines.

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)0.006
metaresearch head score (Gemma)0.015
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 score0.017
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.006
Science and technology studies0.0070.033
Scholarly communication0.0130.009
Open science0.0010.010
Research integrity0.0020.002
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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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

Citations28
Published2009
Admission routes1
Has abstractyes

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