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Record W4321599361 · doi:10.4000/insitu.37415

Modern heritage and housing renovation: Policy development and practical experiences from Gothenburg, Sweden

2023· article· en· W4321599361 on OpenAlexaff
Paula Femenías, Sanja Peter, Mattias Legnér

Bibliographic record

VenueIn Situ · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsCanadian Heritage
FundersEnergimyndigheten
KeywordsModernization theoryArchitectureContext (archaeology)EconomyStock (firearms)HumanitiesCultural heritagePolitical scienceSociologyHistoryLawArtEconomicsArchaeology

Abstract

fetched live from OpenAlex

Post-war housing stocks have been in focus for modernisation and transformation since the 1980s. Technical deficiencies and social problems related to exclusion and segregation have been arguments for investments. The architecture has been seen as part of the problem and alterations to its character have been important in finding solutions. Lately, policy for energy efficient renovation and decarbonisation of the housing sector has put the modern housing stock in focus again. With reference to the lack of common appreciation and understanding of the historical and cultural value of the post-war housing, this paper discusses current policy and its implementation. The paper begins by looking at Gothenburg, the second largest city in Sweden. The development of a Modern Historical Environment program is presented with its application in three examples of housing. These cases exemplify the opportunities and consequences of modernisation and energy renovation on modern heritage. The designation of modern built heritage differs from the designation of older constructions due to its scale and volume. Designating an object refers, on the one hand, to recognising an example of a specific building type and construction methods and, on the other, to its socio-historical context. Thus, both tangible and intangible values are acknowledged. Modern heritage is characterised by its resilience to alterations and allows layers of change to be included, informing about historical events.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.005
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0030.002
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 head0.186
GPT teacher head0.313
Teacher spread0.126 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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