Arthur Erickson on Learning Systems
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".