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Record W4387530072 · doi:10.3390/su152014734

Partnership Structure and Partner Outcomes: A Comparative Study of Large Community Sustainability Cross-Sector Partnerships in Montreal, Barcelona and Gwangju

2023· article· en· W4387530072 on OpenAlexafffundabout
Amelia Clarke, Valentina Castillo Cifuentes, Eduardo Ordonez‐Ponce

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsAthabasca UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Waterloo
KeywordsGeneral partnershipSustainabilityCivil societyPrivate sectorPolitical scienceEconomic growthBusinessRegional scienceGeographyEconomicsPoliticsFinance

Abstract

fetched live from OpenAlex

The aim of this research was to understand the structural features of large cross-sector social partnerships (CSSPs) and their resulting partner outcomes. This study analyzed and compared the partnership structures of three large CSSPs, each from a different continent: Barcelona + Sustainable in Barcelona, Spain; Gwangju Council for Sustainable Development in Gwangju, South Korea; and Sustainable Montreal in Montreal, Canada. Based on a survey of the partners in each of the three partnerships, the partner outcomes were also determined and compared. Building on these findings and using abductive analysis, the relationships between the partnerships’ structural features and partner outcomes are considered. An updated set of seven structural features for studying large cross-sector partnerships is offered. The empirical findings show some differences between the partnership designs and between the partner outcomes of the three partnerships. The experiences of the civil society, private sector and public sector partners in each of the cases were relatively similar, showing that in large partnerships, the sector was less relevant than in small partnerships.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.119
GPT teacher head0.367
Teacher spread0.248 · 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 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

Citations4
Published2023
Admission routes3
Has abstractyes

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