Accessibility and Inclusiveness Policies for Public Open Spaces in Fragile Urban Contexts: Official Discourses and Actors’ Perceptions in Kaya, Burkina Faso
Bibliographic record
Abstract
Sustainable urban development calls for policies that ensure safe and inclusive access to public open spaces, yet little is known about implementation in fragile urban contexts.Focused on Kaya, a fragile city of Burkina Faso, this study examines how official discourses and the perceptions of urban public action actors explain the challenges of implementing policies for accessible and inclusive public open spaces (POS).We conducted 14 unstructured interviews with urban actors and analysed 8 urban policy and planning documents from 2006 to 2024.Data were coded in NVivo 15 using a hybrid analytic approach combining a deductive framework derived from the research question and literature with inductive coding for emergent themes.Two recurrent patterns stand out: (i) diversion of planned public open spaces to other uses; and (ii) the persistence of undeveloped, non-functional, public open spaces, revealing a gap between the official discourse, which reaffirms the right to the city and spatial justice, and the perceived reality.Drawing on these findings, we propose an actionable framework for fragile cities that implies (1) publicising and enforcing land-use rules; (2) reserving space for informal activities to free land for POS and promote multifunctional spaces; and (3) prioritising accessibility/inclusiveness on political agendas.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".