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Record W2998531152 · doi:10.5539/jms.v10n1p1

The Role of Trans-Disciplinary Research in Sustainable Renovation

2019· article· en· W2998531152 on OpenAlexvenueno aff
Kristina Mjörnell

Bibliographic record

VenueJournal of Management and Sustainability · 2019
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
FundersVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsSustainabilityWork (physics)RelocationProcess (computing)DisciplineBusinessEngineeringEnvironmental planningArchitectural engineeringSociology

Abstract

fetched live from OpenAlex

The paper discusses the role of trans-disciplinary research networks tackling the challenges of sustainable renovation such as; environmental impact of substitute building materials and waste, relocation of tenants, lack of skilled labor, rent increase due to high renovation costs, and provides a detailed perspective on the effects in terms of both new forms of collaboration and research results obtained by the researchers and practitioners within the network. The research network Sustainable Integrated Renovation SIRen has become a platform for researchers and actors such as building owners, housing companies, facility managers, contractors, consultants, architects, building conservationists, authorities and tenants’ organisations to meet and work together on technical, environmental, economic, social and cultural historical aspects on renovation of buildings, as well as to identify and discuss new challenges. A multi-aspect process covering all aspects that must be considered by the various actors during different stages of the renovation process has been developed and implemented in four ‘Living Labs’ in real renovation projects. This involved using new modes of work in early stages to place the focus on sustainability aspects and work on new dialogue methods and using methods to evaluate the various renovation options based on technical, environmental, economic, social and cultural historical perspectives.

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)0.005
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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