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Record W4233954703 · doi:10.22215/etd/2021-14514

Embracing the Air: The Sensory Experience of Hemp-Lime Architecture

2021· dissertation· en· W4233954703 on OpenAlexaboutno aff
Robin Papp

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsMateriality (auditing)ArchitectureReverenceAestheticsLimeVisual artsEngineeringArtArchitectural engineeringMaterials sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Through this research, I explore the multi-sensory properties of hemp-lime construction as an antidote to the overly visual experience of contemporary architecture. I begin design from the material and elaborate on the senses involved with the dynamics of hemp walls: the airy trio of humidity, thermal and sound. I dissolve the complexity of a conventional wall, where the layers act separately, and compare this to a hemp-lime composition which acts in unison. Throughout, my research strives to be environmentally responsible with methods of construction that consider the lifecycle of the material; from the conception of a carbon-negative product to the built experience, and to its afterlife of being recycled or fully decomposed. Alongside the historical significance of hemp and lime, the project works at crafting a space of reverence, located in the sacred site of St Raphael's Cemetery (Ontario), that cleanses the occupant's senses through materiality.

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)0.001
metaresearch head score (Gemma)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.008
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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