MétaCan
Menu
Retour à la cohorte
Enregistrement W2153954743

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

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

Notice bibliographique

Revue˜The œinnovation journal · 2012
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePublic Policy and Administration Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublic relationsWorkforceCollaborative governanceGeneral partnershipGovernment (linguistics)Political scienceCorporate governanceBusinessManagementEconomicsLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,063

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,008
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0050,003
Études des sciences et des technologies0,0080,026
Communication savante0,0160,019
Science ouverte0,0040,010
Intégrité de la recherche0,0050,004
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,125
Tête enseignante GPT0,393
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations198
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revue˜The œinnovation journalMême sujetPublic Policy and Administration ResearchTravaux en français237 207