MétaCan
Menu
Back to cohort
Record W6986894435

(Re)thinking public school architecture as a pedagogical tool

2021· dissertation· en· W6986894435 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2021
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureContext (archaeology)Process (computing)PoliticsSchool educationEducational research
DOInot available

Abstract

fetched live from OpenAlex

This thesis aims to rethink elementary public-school architecture by exploring its ability to become an influential aspect of the pedagogical process in schools. As educational paradigms have historically responded to social, political, and cultural conditions, it appears that the development of educational paradigms has moved faster than the educational buildings of the 21st century. Paradoxically, the spatial conditions of educational architecture seem to be stuck in the 19th century. Although there are notable school buildings that emerged from the 20th and 21st century that challenge a conventional school model, the existence of a gap between school architecture and pedagogical paradigms is predominant in the North American context. Beginning with an investigation of the current spatial conditions of educational architecture, specifically in North America, this thesis analyzes the relationship between school buildings and pedagogical paradigms that draw upon the history of education and its built institutions. As well, it examines the factors that prevent such correlation. Relevant building typologies were studied through orthographic drawings to create a visual comparison of school buildings from the 19th century to today. This allows us to observe the major spatial transformations that occurred between school models over time. Additionally, the analysis addresses how the social, economic, and political factors influence the relationship between the design of learning environments and the shift in educational paradigms, uncovering the principles of school designs and identifying clear discontinuities between the built forms and educational models. Undoubtedly, most of the contemporary educational buildings present in the North American context manifest spatial traditions that bear few relations to the current knowledge of the learning processes. Considering the significant role of the learning environment in the support of critical thinking, discovery, and creativity, this thesis explores this potential to overcome century-old traditions of learning through memorization and subservience to the authority of the teacher. We use the context of Markham, Ontario, in the Greater Toronto Area, to create an elementary school based on the principles seen in Montessori’s, Reggio Emilia Schools, and Lab Ecole projects, which respond to the basis of the most actual theories of children education. The designs we see today of newly constructed school buildings within the suburban context tend to be an afterthought, prioritizing budget, and fast construction rates with little to no consideration to how the built environment can aid in the learning process. As a result, the suburbs provide an ideal setting to explore how the physical environment can aid in the learning process. Ultimately, using architecture as a pedagogical tool that prompts the physical environment to inspire, stimulate, and encourage exploration and investigation of new ideas while supporting collaboration and the development of connections beyond the typical school environment.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.021
Scholarly communication0.0100.008
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Explore more

Same venueLu Zone Ul (Laurentian University)Same topicMachine Learning in BioinformaticsFrench-language works237,207