Bibliographic record
Abstract
How Heritage Learns explores the dynamics that come into play when public housing becomes valourised as heritage in the Netherlands and how that, in turn modulates the evolution of this protected housing. It builds on the foundation set by the thesis of Steward Brand, that buildings learn through the adaptation of their fabric to external forces: changing fashion, technologies and economy. This dissertation investigates different key drivers for change: Energy, Economy and Comfort (2E+Co). To understand how and why the housing heritage evolved over time, an ecology of ideas is developed that sees buildings as organisms evolving and learning in their environments, providing a multi-sided theoretic model for analysis. Three case studies are extensively explored: the Justus van Effen Quarter in Rotterdam (1921–22) and the King’s Wives of Landlust (1937–38) and Jeruzalem public housing complexes (1949–52), both in Amsterdam. These are all exemplary monuments of Dutch public housing and all three have undergone repeat renovations since their construction. The research not only highlighted their various learning cycles, but also uncovered exciting new information on their origins and histories. What sets public housing heritage apart is the presence of a Story. However, the case studies reveal that the Stones were modulated by dominant 2E+Co ambitions common to all public housing. Above all, How Heritage Learns shows that past promises of increased performance and efficiency were never fulfilled. Without structured reflective observation we are doomed to repeat the same mistakes. Such lessons are all the more important at a time when the built environment stands at the cusp of another revolution driven by environmental imperatives.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
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".