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Record W2108628556

Building Effective Community-University Partnerships: Are Universities Truly Ready?

2011· article· en· W2108628556 on OpenAlexaboutno aff
Susan Eckerle Curwood, Felix Munger, Terry Mitchell, Mary Mackeigan, Ashley Farrar

Bibliographic record

VenueThe Journal of the Abraham Lincoln Association · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipScholarshipService-learningCommunity engagementPublic relationsSociologyInstitutionalisationService (business)Political sciencePedagogyBusinessMarketing
DOInot available

Abstract

fetched live from OpenAlex

Universities and flinders have become progressively more interested in knowledge transfer and the links between universities, democracy, and civic engagement (Ostrander, 2004). This interest has led to increase in community service learning (CSL) programs that ground academia in 'real-world' knowledge and actively contribute to the improvement of local and national social conditions (Ostrander, 2004). There has also been increase in community-based research (CBR) (e.g., Israel, Schultz, Parker, Becker, Allen, & Guzman, 2003). As early as 2003, Strand, Marullo, Cutforth, Stoecker, and Donohue predicted that combining CBR and CSL would be the next important stage of service-learning and engaged scholarship (p. 6), asserting that there is value in extending CSL models to include CBR approaches. In this article we describe a Community Psychology doctoral level course that students complete over a period of three years and that involves them in a CBR partnership with a local anti-poverty organization. In the current paper, we are concerned with the formation of the community-university research partnership rather than the findings of the CBR project itself, which will be reported elsewhere. The purpose of this paper is to contribute to the literature on university and institutional readiness when partnering with community organizations for CBR. Using our doctoral level course as example, we describe the challenges and key learnings in the early stages of developing a community-university partnership and propose methods of addressing the challenges. In particular, this paper is attempting to move beyond general discussions about institutionalization--described by Furco and Holland (2004) as the intentional incorporation of CSL throughout the institution--to assessing readiness for collaboration. We begin with a brief overview of the literature on community-university research partnerships as linked to CSL and CBR. We then describe the context of the current educational initiative in terms of the disciplinary and institutional environment, the early stages of partner identification and partnership formation, and the team research experience in community-engaged, collaborative research on poverty reduction. We end the article identifying key learnings about partnership readiness and a framework for assessing university readiness at three levels: contextual, between-group, and within-group. Background Community Service Learning Community service learning is defined by the Canadian Alliance for Community Service-Learning as an educational approach that integrates service in the community with intentional learning activities (2006, p. 1). In effective CSL initiatives, members of educational institutions and community organizations work together toward mutually beneficial outcomes. While other forms of community-based learning often passively link students to the community in a classic charity model, CSL has become a vehicle to promote genuine, collaborative, community engagement benefitting students, faculty, and community. For example, Boyer (1996) envisions CSL as a vehicle for connecting the rich resources of the university to our most pressing social, civic and ethical problems, to our children, to our schools, to our teachers and to our cities ... (p. 21). Marullo and Edwards (2000) promote a social justice approach to critical education and community-engaged scholarship, and envision transforming university operations in such a way as to allow students and faculty to function as change agents in the community. These contemporary visions of CSL seek to promote learning that addresses social problems at their root causes rather than simply ameliorating their negative impact. Similarly, Strand et al. (2003) suggest that CBR can serve as a vehicle to identify and alter the structural and institutional practices that produce social and economic inequalities. Nyden (2009) further emphasizes the transformative potential of CBR: . …

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.013
Scholarly communication0.0270.043
Open science0.0040.036
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0170.004

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 head0.073
GPT teacher head0.289
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations63
Published2011
Admission routes1
Has abstractyes

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