The Role of Knowledge Infrastructure in Regional Economic Development: The Case of the Research Triangle
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Bibliographic record
Abstract
This article is a case study of the economic transformation of a region recently based on the textiles, furniture, and tobacco industries, into one of the world's leading technopoles, within a period of less than forty years. The case is about what is now known as the Research Triangle region of North Carolina. While many observers focus on the creation of the Research Triangle Park in the late 1950s as the key event, this paper argues that it was the public and private investment in the region's broader infrastructure that stimulated and nurtured the structural transformation. We describe the region's infrastructure as a set of organizations and institutions that are linked together and indicate the roles each of the specific elements has served within the overall network Cet article est une etude de cas de la transformation economique de la region de Raleigh-Durham-Chapel Hill, Caroline du Nord, aussi appelee Research Triangle. L'etude montre comment cette region est passee en quarante ans d'une economie reposant sur les industries du textile, du meuble et du tabac a celle d'une economie du savoir de calibre mondial. Alors que plusieurs observateurs supposent que l'economie du savoir de la region a pris naissance a la fin des annees 1950 suite a la creation du Research Triangle Park, nous croyons que c'est plutot l'investissement de capitaux publics et prives qui en sont a la source. Nous decrivons les infrastructures du savoir de la region comme etant un ensemble d'organisations et d'institutions reliees entre elles. De plus, nous faisons etat du role de ces dernieres au sein de ce vaste reseau. Introduction A few weeks after moving to the Research Triangle region back in 1982, the author was driving from Chapel Hill to Durham on a Friday afternoon and noticed the sign on the roadside. It said, Welcome to Durham, City of Tobacco. Three days later, I happened to be driving the same route doing another errand. Upon reaching the boundary line between Chapel Hill and Durham, the old sign was gone and a new one had been put up. The new one said: Welcome to Durham, City of Medicine. The abrupt change in the welcoming sign is a wonderful metaphor for how the economic development process has unfolded in this region over the last fifty or so years. There are probably few regions in the United States that have undergone such a vivid structural transformation within such a relatively compressed period of time. Although many descriptions of the Research Triangle region have focused on the role of the Research Triangle Park in the region's economic transformation, the thesis presented here is that the investment in the region's infrastructure has been the basic propulsive factor in the region developing into one of the world's leading high tech centres. The gains in economic well-being, in turn, allowed further investments in the region's a process of cumulative causation to use Myrdal's (1957) term. A region's however, varies not only in its scale and efficiency, but also qualitatively in its components and how they are linked. So we will also relate how the particular character of the region's infrastructure has shaped its economic development structure and outcomes. The concept of a knowledge infrastructure, as we shall see, has roots in the European regional development literature on milieu and creative regions, as well as in endogenous growth theory inspired by Romer, Krugman, and others. It is also closely related to other concepts discussed in the literature including technology infrastructure and regional innovation systems. The article is organized as follows. First, we briefly review the relevant regional development literature to place the concept of a literature in context. Then, we begin the empirical section of the paper with an historical overview of the emergence of the Research Triangle region as a high-tech economy, and include some indicators of the path of its regional economic development over the last 50 or so years. …
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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