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Record W6940348931 · doi:10.11573/2rqt-kv41

Arthur Erickson on Learning Systems

2024· book· en· W6940348931 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2024
Typebook
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsParallelsThe artsHigher educationCompartmentalization (fire protection)DisciplineSpace (punctuation)

Abstract

fetched live from OpenAlex

Whether he was designing buildings and spaces for universities, museums, performing arts venues, or libraries, Arthur Erickson was preoccupied with intersections – of people, of cultures, and of ideas. Published by Concordia University Press and the Canadian Centre for Architecture, Arthur Erickson on Learning Systems collects writings by an architect advocating for interdisciplinary approaches to education and the methods for sharing knowledge. In essays on one of his mid-1960s masterpieces, the Simon Fraser University campus, Erickson explains how he intended to avoid compartmentalization between academic disciplines by thinking of a campus as akin to a “biological system” capable of adaptation. He outlines how his design placed a spine through the campus to circulate people – and communication between them – while making space for additional buildings as they became needed. These writings also show Erickson reflecting on whether his original vision was maintained by future development on the site and considering how university education changed in the decades that followed. An introduction by Melanie O’Brian nuances Erickson’s big-picture thinking. She draws parallels between curatorial practices and his approach to learning spaces, and she discusses the experiences of campus users following university expansion and increased specialization among academic disciplines.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.043
GPT teacher head0.321
Teacher spread0.279 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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