Dynamism of Manufacturing SMEs in the North-Atlantic Islands: A Case Study
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Bibliographic record
Abstract
Research into industrial districts has enabled attention to be redirected towards the Marshallian externalities relating to the economies of conglomeration generated by the concentration of businesses on the same site and in the same sector (Beccatini 1992). These economies of conglomeration are in fact the set of benefits obtained by businesses grouped together in a city area, regardless of sector (Tellier 1993; Polese 1994). Geographical proximity helps reduce transaction costs for businesses, which are thus able to take advantage of what Perrin (1990) refers to as territorial synergy. Similarly, Proulx (1991) mentions the benefits of urban areas over rural areas, including concentrations of better financial, brokerage and transportation services and the existence of networks that facilitate contacts and provide information on market development. This same view is also taken by Gofette-Nagot and Schmitt (1998), who postulate that proximity permits interactions between physical and human resources, thus allowing businesses to cope better with national and international competition. Together, these various considerations give some idea of the handicaps or difficulties faced by businesses in rural and island environments outside the major cities. In recent years, the number of studies of rural businesses has grown considerably, suggesting, as we have already shown (Joyal and Deshaies 1998, 2000), that thanks to the contribution of new technologies, even businesses that are geographically isolated are able to function well. Davidson et al (1994) note that in Sweden, as far as proximity of markets and supply sources is concerned, the facilities available in local infrastructures and the availability of financial assistance are still important elements. However, like Nelson (1998), we believe we have shown that the absence of economies of conglomeration in rural environments is no longer an insurmountable obstacle. It was this observation that led us to compare the situation of SMEs (Small and Medium-sized Entreprises) in island environments and in rural or non-city environments, using a case study to see whether a specific form of dynamism actually exists. Development conditions in island environments are known to be similar to local development conditions. As Cote (1996, 1997, 2000) pointed out, in many such environments social players must learn to rely on their own means and organisational skills in order to meet their own needs. At first glance, it is easy to identify similarities between small and medium-sized enterprises with island locations and those in rural communities. Both are situated at a distance from major decision-making centres and information centres. Their remote locations provide an additional challenge, since they are unable to take advantage of the territorial synergic effects available to city businesses, and there is no local dynamic likely to create an impetus. According to Falcone et al (1996), the greatest obstacle facing rural entrepreneurs is their relative isolation. It is easy to understand why -- they simply cannot count on the same support and assistance as their counterparts in cities or more densely populated areas. The goal of this paper is to see whether the characteristics of rural SMEs also apply to island SMEs. We begin by presenting their characteristics against the background situation, and go on to identify some research guidelines. We then describe the survey methodology, the challenges and dynamism of the SMEs studied for the research, and the factors underlying their entrepreneurial vitality. The Backgound Situation According to Illouz-Winiki and Paillard (1998), a rural area is an environment in which the population is scattered in small towns or villages over a relatively large area that is sometimes, but not always, less developed economically than the other regions of a given country. Rural areas are also distinguished by the presence of primary activities in the natural resource sector, such as farms, outdoor leisure activities, sandpits and gravel quarries, agro-tourism firms and so on. …
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it