Designing For Human Dignity: New approaches to the inclusivity of homeless and vulnerably housed communities
Bibliographic record
Abstract
This presentation is an exploration of my doctoral research, Designing for Human Dignity: New approaches to the inclusivity of homeless and vulnerably housed communities. The research addresses questions regarding the inclusivity of the homeless and vulnerably housed community within urban design, focusing on the regeneration of the town centre of Eastbourne, UK. The research aim is to reframe and re-characterise how homeless communities are systemically positioned in urban design through the lens of human dignity. Currently, elements of these themes have been taken into consideration by the Design Council CABE (Fletcher, 2006) and in the Localism Act (Clarke, 2011). However, surveys and consultation periods predominantly offered to certain groups with fixed addresses and access to modern technology breed a gap where considerations of people without access to these are included. This work-in-progress presentation will share the findings of the research completed to date. The research grew from both my fascination and curiosity with changes made in public spaces. Experiencing the regeneration of my hometown, Eastbourne, I witnessed on a micro-scale how the built environment changed in response to issues surrounding anti-social behaviour. On a macro-scale, there is a common thread in changes made in public spaces within the United Kingdom. The systemic response tends to be removing planters, benches, and sheltered doorways to decrease criminal activity in the hope of providing safer streets (Cosgrave, 2023). Two prominent themes that have been revealed so far are language and perception. In the presentation I will be focusing on how these themes are entangled within urban design and planning systems. Specifically, it examines how language and perception play a role in both perpetuating and disrupting the current discourse surrounding the inclusivity of people experiencing homelessness.
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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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.091 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".