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Record W2788256328 · doi:10.3390/su10020558

Cross-Sector Social Partnerships for Social Change: The Roles of Non-Governmental Organizations

2018· article· en· W2788256328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSustainability · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBusinessSocial enterpriseSocial changePublic relationsPolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Complex social and environmental issues call for broader collaboration across different sectors so as to instigate transformative social change. While previous scholars have emphasized the role of non-governmental organizations (NGOs) in facilitating social change, they have not provided a nuanced assessment of NGOs’ different roles. We use the Poverty and Employment Precarity in Southern Ontario (PEPSO) research partnership as a study case and explore NGO partners’ different roles in a large cross-sector social partnership (CSSP). By interviewing 12 NGO partners and 4 non-NGO partners involved in the PEPSO research partnership, our research results show that NGOs primarily have 10 roles in a CSSP. They include enabling roles such as consultant, capacity builder, analyst, and funder; coordinating roles such as broker and communicator; and facilitating roles such as initiator, leader, advocate, and monitor. These roles allow NGOs to fulfil their duties to make substantial contributions to a CSSP.

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.

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)0.001
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 score0.206
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.095
GPT teacher head0.330
Teacher spread0.234 · 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