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Record W4384202343 · doi:10.3390/land12071400

The Co-Production of a Shared Community Space in Al-Khodor, Karantina, in the Aftermath of the Beirut Port Blast

2023· article· en· W4384202343 on OpenAlexfundno aff
Howayda Al-Harithy, Batoul Yassine

Bibliographic record

VenueLand · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsnot available
FundersUniversity College LondonInternational Development Research Centre
KeywordsCitizen journalismIntervention (counseling)Government (linguistics)Port (circuit theory)Space (punctuation)SociologyWork (physics)Sociocultural evolutionPublic spacePsychological interventionEnvironmental planningEconomic growthPublic relationsGeographyPolitical scienceEngineeringArchitectural engineeringPsychologyComputer scienceAnthropology

Abstract

fetched live from OpenAlex

This paper explores urban recovery as a participatory bottom-up process that highlights the importance and social significance of spaces of shared memories in reconstituting the built as well as the sociocultural fabrics of a place. It examines the multiple modes of engaging local communities in the process of recovering and rehabilitating shared public spaces, including organizing workshops to identify a space of common social significance, co-designing and co-producing a spatial intervention, and maintaining the intervention over the long term. The paper focuses on Karantina, a neighborhood in Beirut that became the site of post-disaster recovery in the aftermath of the Beirut Port blast in August 2020, and the spatial intervention that the urban recovery team at the Beirut Urban Lab implemented in the sub-neighborhood of Al-Khodor. In doing so, the paper contributes experiences from recent work on participatory modes of engaging the local community groups in Al-Khodor. It highlights the importance of community participation in researching, designing, implementing, and maintaining spatial interventions in the near absence of an active government in a country such as Lebanon.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.330
Teacher spread0.286 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

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