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Record W4307977157 · doi:10.1007/978-3-030-93072-1_4

Co-productive Urban Planning: Protecting and Expanding Food Security in Uganda’s Secondary Cities

2022· book-chapter· en· W4307977157 on OpenAlexaff
Andrea M. Brown

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSlumEmpowermentFood securityCorporate governanceUrban planningGovernment (linguistics)Context (archaeology)BusinessEconomic growthEnvironmental planningPolitical scienceGeographySociologyPopulationEconomicsEngineering

Abstract

fetched live from OpenAlex

Abstract Co-production, a strategy increasingly being adopted by urban planners, has potential for protecting and expanding urban food security. Its goals go beyond those of participation to include substantive sharing in policy design, implementation and monitoring: shifting some power associated with these decisions and actions to primary stakeholders. Co-production is desirable for empowerment outcomes, and also on grounds of greater efficiency, cost savings and more locally informed planning. Slum/Shack Dwellers International (SDI) is a lead actor in co-production and has partnered with the Government of Uganda, working on pro-poor urban development projects underway in several secondary cities, including Jinja and Mbale. SDI frames slum dweller advocacy in a rights-based discourse with provisions that informal settlement residents articulate their own priorities. Given food access is a central priority of the urban poor, co-production creates opportunities to address urban food insecurity. However, governments, including municipal and national governments in Uganda, resist genuine power sharing with urban slum dwellers. This research explores how co-production engages slum dwellers and governance actors in the secondary cities of Jinja and Mbale, Uganda. It seeks to understand the possibilities and limitations of the current SDI co-productive programing in the context of urban food security. Empirical evidence to support this research is drawn from interviews with urban planning stakeholders in Kampala, Jinja and Mbale in 2015 and 2018.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.056
GPT teacher head0.291
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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