A Comparison of the Industrial and Market Characteristics of Canadian and US Firms in the Commercial Geographic Information Systems (GIS) Sector
Bibliographic record
Abstract
Abstracts: Comparison of the Industrial and Market Characteristics of Canadian and US Firms in the Commercial Geographic Information Systems (GIS) Sector. This paper compares the industrial, technological, and market characteristics of Canadian and US firms in the commercial geographic information systems (GIS) sector. Evidence from a postal survey suggests that Canada's GIS sector differs significantly from its counterpart in the US. Specifically, Canadian firms tend to be more export-intensive, despite being less oriented toward new product/service development. In addition, many Canadian firms have developed export specialisations that mirror the resource-based nature of the Western and Maritime provinces. In terms of export destinations, US firms mainly serve markets in Canada and Mexico, whereas Canada's exports more typically go to developing countries and/or newly emerging markets. A further contrast is that Canadian firms are more likely to forge research partnerships with compl ementary firms (notably US ones). The paper concludes with a brief discussion of the nature of the Canadian GIS sector relative to its US counterpart. ********** In both Canada and the United States, the commercial geographic information systems (GIS) industry represents a small but fast growing sector that depends upon high quality human capital, sustained research and development (R&D) activity and continuous innovation (Mark 1999). Although the industry is globally dominated by three US corporations (i.e. ESRI, MapInfo and Intergraph), high rates of new firm entry in recent years suggest that considerable growth potential remains (Hartung 1999). In Canada, for example, close to 30 % of the current population of GIS companies did not exist prior to 1990 (the corresponding estimate for the US is just over 40 %). This young sector consists of a corporate core (the Big Three), surrounded by an expanding population of niche players that operate across diverse fields (e.g. marine navigation, epidemiology, crime analysis and remote sensing). Despite the importance of this sector's outputs to a wide range of users, few scholars have paid much attention to the history or growth dynamics of this industry (see Coppock and Rhind 1991). While worldwide sales from the GIS sector are currently estimated at only US$7 billion (i.e. total output is less than Microsoft's 2001 earnings), global sales are projected to increase at a compounded rate of more than 20% per annum over the next 10 years (National Academy of Public Administration 1998). Accordingly, there is good reason to suspect that the GIS industry will play an increasingly central role in the information economy of the 2000s (Leinbach 2001). This said, the GIS industry is difficult to identify on the basis of Standard Industrial Classification (SIC) codes, Standard Industrial Trade Classification (SITC) codes, or commercial/industrial directories. Part of the problem stems from the fact that the GIS sector spans both the industrial and service components of the economy. For example, commercial GIS outputs include specialised hardware (e.g. digital scanners), advanced software (e.g. mapping programmes), customised databases, consulting services, or any combination of these. The very term 'GIS' is also hard to define, in that multiple definitions can be found in the academic and commercial literature (see Pickles 1995). While more will be said about this later, it should be stressed from the outset that the GIS sector consists of several activity sets. Leaving definitional complexities aside for now, the main goal of this paper is to compare the demographic, industrial, and technical characteristics of Canadian and US firms in this relatively new sector. The underlying theoretical backdrop is that firms in new or technologically dynamic sectors must innovate on a frequent basis to maintain their competitiveness (Porter 1990). At the same time, it is widely thought that technical collaboration with external partners can accelerate or facilitate the innovation process, especially among new companies (Freeman 1991, Karlsson 1997, Malecki 1996). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".