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Record W2793150647 · doi:10.1080/02665433.2018.1423638

Hans Blumenfeld: a moderate defence of expertise in the controversial 1960s

2018· article· en· W2793150647 on OpenAlexafffundabout
Frédéric Mercure-Jolette

Bibliographic record

VenuePlanning Perspectives · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversité de Montréal
FundersCore Research for Evolutional Science and TechnologySocial Sciences and Humanities Research Council of Canada
KeywordsTechnocracyCriticismGermanSociologySkepticismPlannerPaternalismLawHistoryAestheticsEnvironmental ethicsEpistemologyPolitical sciencePhilosophyPoliticsArchaeology

Abstract

fetched live from OpenAlex

Despite an impressive international career, the German city planner Hans Blumenfeld remains largely unknown. In The Transatlantic Collapse of Urban Renewal, Christopher Klemek depicts him as an apologist of authoritarian and technocratic urban renewal. This statement is puzzling because many post-war planners saw him as a humble left-wing progressive. Drawing from a close analysis of the writings and actions of Blumenfeld, in particular his work in Canada, this article shows that he was far from being an uncritical apologist of urban renewal in North America. He was always very sceptical but, as opposed to Jane Jacobs, he did not think that modernist urban planning had it all wrong. He always thought that progress came not only from citizens’ participation, but also principally from science and expertise. For that reason, Blumenfeld tried, on the one hand, to transform the language of city planning to answer criticism from citizens and activists, and, on the other, to retain the expertise to proceed from plan-making to a communication-process, as some professionals were beginning to advocate at the time.

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)0.005
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.064
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0150.039
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.041
GPT teacher head0.353
Teacher spread0.311 · 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

Citations1
Published2018
Admission routes3
Has abstractyes

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