A compilation of history & theory dissertations by third year students from SAUL II
Bibliographic record
Abstract
1 Architecture of The Self and The Within, Naomi Panter - 2 Mallow, D€v€lopm€nt Strat€gy 2010-2016? Wh€r€ Do€s it all go wrong? Eamonn Kelly - 3 Ich Bin. Wer? (I am. Who?), Briain Moriarty - 4 The Effects of an Economic Downturn on Society and Architecture, Silvia Zheleva - 5 Audio-Spatiality Understanding Aural Architecture, James O’ Donovan - 6 Fashion and Architecture, Sinéad MacMahon - 7 All Hail Frankie P. Toronto - Palladio is dead! Objects of Alternating Architectures, Ben Mullen - 8 Symbols, Diarmuid O’Sullivan - 9 Modern Landscape Architecture in Post War America, Clare Reidy - 10 Influences of landscape architecture in architecture, Michael McLaughlin - 11 Perceptions of the future. Where’s my hoverboard? Ian MacDonald - 12 A Student’s Guide to Venturi and Eisenman. It’s easy to say with hindsight but who really thought postmodernism was a good idea? Eugene O’Callaghan - 13 Passing go Excursions in Irish Planning Economics, Gerard Walsh
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 | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.022 |
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".