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Record W2153954743

Stewards, Mediators, and Catalysts: Toward a Model of Collaborative Leadership1

2012· article· en· W2153954743 on OpenAlexvenueno aff
Christopher Ansell, Alison Gash

Bibliographic record

Venue˜The œinnovation journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsWorkforceCollaborative governanceGeneral partnershipGovernment (linguistics)Political scienceCorporate governanceBusinessManagementEconomicsLaw
DOInot available

Abstract

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ABSTRACTLeadership is widely recognized as an important ingredient in successful collaboration. Collaborative leaders typically play a facilitative role, encouraging and enabling stakeholders to work together effectively. Building on the existing literature on collaborative governance and interviews with leaders of U.S. Workforce Investment Boards, we identify three facilitative roles for collaborative leaders. Stewards facilitate collaboration by helping to convene collaboration and maintain its integrity. Mediators facilitate collaboration by managing conflict and arbitrating exchange between stakeholders. Catalysts facilitate collaboration by helping to identify and realize value-creating opportunities. Although collaborative leaders are called upon to play multiple roles, the salience of these roles may vary with the circumstances and goals of collaboration. In situations of high conflict and low trust, for example, collaborative leaders may be called upon to emphasize steward and mediator roles. In situations where creative problem-solving is the primary goal, the catalyst role may become much more central. Distinguishing these three collaborative leadership roles is an important step toward building a contingency model of collaborative leadership.Keywords: collaboration, collaborative governance, stakeholder, contingency, leadership, workforce development.IntroductionIn 1998, President Clinton signed into law the Workforce Investment Act (WIA). Much like the welfare reform, enacted only two years earlier, WIA promised to revolutionize the work of workforce development. Although the federal government had long been a supplier of workforce training programs under programs enacted through the Job Training Partnership Act (JTPA) or the Comprehensive Employment and Training Act (CETA), these programs offered a patchwork approach to job training. According to former Labor Secretary Alexis Herman, these programs were -never fully brought into alignment with other components of the system'. Consequently, federally funded job training programs were largely scattered - offering clients limited access to services, career advice, quality job information data, and skills training.2 It was hoped that through coordination and co-location at the servicedelivery level (e.g., one stop shops), consumers would have easier access to every element of the workforce development system, from simple job searches to receiving advice on career planning, to enrolling in basic more advanced skills training. However, coordination of service delivery was only one of the problems plaguing an increasingly dysfunctional workforce system, so policymakers also mandated a more comprehensive strategy of collaboration. The WIA placed control of each local workforce area (established by governors) in Workforce Investment Boards (WIBs), which would be jointly governed by labor unions, community colleges, training providers, locally elected officials, industry leaders, and social service providers. These stakeholders were to develop collaborative strategies to create a more effective workforce system.Despite this mandated collaborative framework, large variations developed in the degree, scope, type, and breadth of collaboration among workforce development areas. Some workforce development areas practiced pro forma collaborative governance - presenting only enough of a veneer of collaboration to please local and federal officials. Others surpassed this by implementing micro-collaboratives- supplementing the WIB's governance with smaller project-based forms of collaborative governance. A small but growing number of WIBs engaged in more extensive collaborative governance. In each of these cases, leaders played a critical role in shaping the depth and extent of WIB collaboration. Leaders of the most collaborative WIBs, for example, have begun to reassess what one referred to as the little fiefdoms' established by governors under WIA - workforce areas established along political rather than economic lines. …

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)0.012
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0080.026
Scholarly communication0.0160.019
Open science0.0040.010
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.125
GPT teacher head0.393
Teacher spread0.268 · 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 designTheoretical or conceptual
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

Citations198
Published2012
Admission routes1
Has abstractyes

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Same venue˜The œinnovation journalSame topicPublic Policy and Administration ResearchFrench-language works237,207