Cluster Consciousness and Branding in the Greater Sudbury Mining Innovation Cluster
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".