Bibliographic record
Abstract
This paper uses ‘deep time’, as an alternative ontology to crisis management to argue for the application of a broad decolonial approach in lieu of contemporary green design practices. Methodologically, this paper substantiates it claims by utilising conventional academic ‘knowledge’ production, as represented in literature, references, and case studies, but also supports the expansion of knowledge through a deeper exploration of place, pattern, and time demonstrated by intermingling deep time principles with Indigenous spatial practices. Fearing that urban life will descend into obsolescence and irrelevance if no such knowledge systems are taken up, this paper proposes an alternative trajectory as a preventive measure, which has all been exacerbated by the ongoing pandemic. By exploring alternative Indigenous design ontologies, specifically in Oceania, alongside deep adaptation and deep time, this paper’s authors intend to provide an important basis for research and teaching that reinvigorates connections to Indigenous epistemologies and knowledge systems. This paper proposes that by taking up notions of deep adaptation and Indigenous epistemologies as critiques of Western notions of time, property, etc. architecture, design and planning might re-situate ideas, ranging from stewardship to maintenance, within time and placebased technologies outside of the discourse of crisis.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".