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Record W1496952816 · doi:10.25916/sut.26223833

Cyclic Functional Collaboration: A scientific approach to housing

2012· article· en· W1496952816 on OpenAlexaboutno aff
Sean McNelis

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessComputer sciencePublic relationsPolitical science

Abstract

fetched live from OpenAlex

Housing research is very diverse operating across many disciplines. It is characterised by a broad range of methods, approaches and purposes. It is also very fragmented with researchers having very little sense of how different types of research relate to one another. If we are to promote collaboration among researchers and find solutions to our pressing housing problems, we need a framework which will hold this diversity together. Housing research is about asking and answering questions. Few researchers, however, reflect upon the questions they ask and the type of answer their questions anticipate. This paper proposes ‘a framework for collaborative creativity’. It contends that if we examine the questions underpinning all these different research methods, we will find that each is primarily oriented towards answering a particular question within a group of eight questions. The paper proposes that a scientific approach to housing consists of asking a complete set of eight inter-related questions: an empirical question, a theoretical question, an historical question, an evaluative/critical question, a transformative question, a visionary/policy question, a strategic question and a practical question. These questions are functionally inter-related, they provide a framework for inter-disciplinary collaboration and, they are ongoing and cyclic producing cumulative and progressive results. Housing researchers can distinguish these eight questions by reflecting upon themselves and their work. The paper draws upon a discovery by Bernard Lonergan, a Canadian methodologist, philosopher, theologian and economist ([1957]1992, [1972]1990).

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.251
Teacher spread0.218 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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