Seattle as a Digital City: Unexpected or Inevitable?/ Seattle Comme Ville Digitale: Imprevu Ou Inevitable?
Bibliographic record
Abstract
Although one of the smallest global cities, Seattle can claim a place as the home of Boeing, Paccar (Kenworth and Peterbilt), Starbucks and Costco, but the city ranks even higher as a digital city, as an initiator of wireless technologies, as the home of Amazon, of a burgeoning bio-technology sector, and above all of Microsoft. This paper traces the transformation of Seattle from a provincial city to a global high-tech player and ultra-wired city, as a consequence of location, key institutions like the University of Washington, innovative entrepreneurs and the characteristics of the people attracted to the region. The paper also examines how this digital revolution has changed the social structure and the economic geography of the region, contributing to the gentrification of the core city of Seattle, and the creation of a high-tech corridor supported by distinctive clusters of specialized business services. Malgre le fait qu'elle soit une des plus petites villes globales, Seattle peut se vanter d'avoir chez elle Boeing, Paccar (Kenworth et Piterbilt), Starbucks et Costco. Mais elle est davantage reconnue comme une ville digitale, une pionniere des technologies sans fil, le siege social de Amazon, d'un secteur de la biotechnologie bourgeonnant et, surtout, de la presence de Microsoft. Cet article retrace la transformation de Seattle, passant d'une ville de province a une ville de haute technologie de renommee globale vue sa localisation, ces institutions universitaires cles, telle la University of Washington, ses entrepreneurs innovateurs et le caractere particulier des gens attires par la region. Cet article examine aussi comment la revolution digitale a change la structure sociale et la geographie economique de la region, contribuant a la gentrification du centre-ville de Seattle et a la creation d'un corridor de haute technologie supporte par des grappes d'entreprises de services specialises. Introduction When I (Richard Morrill) arrived in Seattle in the summer of 1955, to study urban geography, and soon to become immersed in the early days of Regional Science, the city offered not the remotest hint of what was to come. Seattle was an unusually egalitarian, middle class city, highly unionized, and dominated by manufacturing--Boeing aircraft, Kenworth trucks, Todd shipyards, Weyerhauser, US Plywood and other forest product giants. It had the UW (University of Washington) but it was a second tier state university, with a smallish medical school and no teaching hospital. Forty years later, by 1995, Seattle had transformed into one of the nation's and world's most hi-tech, digital, wired, computer-oriented, information rich cities, and, concomitantly, it changed from provincial and egalitarian to a global and far more unequal city, (1) from a suburb-like city of families and a cultural backwater, to a city unusually rich in singles and unmarried partners, and cultural innovators. Like San Francisco, Seattle is fairly extreme on this dimension. The University of Washington similarly emerged as one of the leading research universities, including in computer sciences as well as biomedical research. According to Atkinson's Metropolitan New Economy Index, Seattle ranked 3rd, behind only San Francisco and Austin, as most advanced in the digital economy. (Atkinson 2003) (Table 1). What is the basis for this remarkable transformation? Who and what were the agents of change; what is the particular nature of the hi-tech, information-oriented new economy global place? Who and what has been left behind, but also how fragile is this global city position? This paper briefly reviews the theory of high tech regional development, traces the fairly brief history of hi-tech development and evaluates its global role and significance, then reviews the Seattle story in relation to the theory of high tech development, but also describes the local geography of these sectors of the economy, and the benefits and costs of the transformation. …
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".