Cluster Consciousness and Branding in the Greater Sudbury Mining Innovation Cluster
Notice bibliographique
Résumé
Clusters have been a major area of interest for researchers and policymakers over the last 40 years. Clusters are defined as a “a geographic concentration of interconnected companies, specialized suppliers, service providers, and associated institutions in a particular field that are present in a nation or region” (Porter, 1998, p.78), while increased innovative capacities and economic growth are often cited as their contribution to regional economic development (Porter, 1990; Storper and Walker, 1989). There is a rich body of literature on clusters, including exploring the factors that lead to cluster development, how to create effective policies to support clusters, and understanding the social dynamics of innovation within clusters. However, the way clusters are branded and marketed is an underdeveloped area in the academic and policy literature. That being said, the work that has been done in this area has shown that clusters with effectively managed brands and identities are able to better realize the benefits of being in a cluster (i.e., increased collaboration, innovative capacity, etc.). \nGiven this gap in the literature, the purpose of this research is to explore the challenges and opportunities with creating a cluster identity and brand for the mining innovation cluster in Greater Sudbury, Ontario. Greater Sudbury, Ontario was selected as a case study because it is home to over 200 mining supply and service companies that, combined with regional research institutions and government agencies, make up the Greater Sudbury mining innovation cluster. This cluster is a primary economic contributor to the region and has been a leading employer in the Greater Sudbury area for decades. While this cluster is an important contributor to the regional economy, it has been cited (Robinson, 2004; Hall et. al, in progress ongoing) that the cluster is experiencing a lack of cluster consciousness. In other words, members of the cluster do not recognize themselves as being a part of a cluster. \nIn terms of methods, this research used document analysis and interviews with 13 key informants. This research followed a similar approach to Marauner and Zorn (2017) and used the Triple Helix model to identify key informants in three core areas: postsecondary and research institutions, governmental bodies, and businesses and other related organizations (Etzkowitz, 2002). The results of the interviews showed some ambiguity from cluster members about the existence of a cluster brand. However, there was a general consensus among the interview participants that developing a cluster brand and identity is important to cluster members and they pointed to some key attributes that could be used to develop an effective cluster branding and identity strategy. These include Greater Sudbury’s long history as a centre for mining as well as the region’s expertise in underground, hard rock mining. Key informants also highlighted collaboration, representation, and place as challenges associated with the development of creating a cluster brand and identity. In addition, there were calls for more clearly defined cluster leadership to strengthen the gaps in the cluster brand and identity. Through the calls for stronger, more visible leadership from the key informants and the established importance of cluster governance in the examined literature, one of the major takeaways from this research is the need to establish clear, effective leadership for future cluster branding initiatives in the Greater Sudbury mining innovation cluster.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,017 | 0,008 |
| Communication savante | 0,008 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».