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Record W4205857201 · doi:10.1353/ohq.2005.0021

Planting High-Technology Seeds: Tektronix's Role in the Creation of Portland's Silicon Forest

2005· article· en· W4205857201 on OpenAlexaboutno aff
Heike Mayer

Bibliographic record

VenueOregon Historical Quarterly · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsSilicon valleyDowntownEngineeringArchaeologyAgricultural economicsBusinessHistoryFinanceEconomics

Abstract

fetched live from OpenAlex

Heike Planting High-Technology Seeds Tektronix's Role intheCreation of Portland's SiliconForest Over the past six decades, Portland's economy has changed ? from one that depended on natural resources to one that f values knowledge-creation and innovation. Today, some of themost important employers inPortland are firms thatpro duce high-technology products such as silicon chips, test and measurement instruments, specialized computers, and software. These companies have a significant economic impact on the region, employing more than sixty-six thousand people and paying an average annual wage of $78,466.1 The heart of the Silicon Forest ? a name applied in the early 1980s to the concentration of high-technology firms around Portland (and a reference to the Silicon Valley in Santa Clara County, California) ? is in Washington County, west of Portland, where firms such as Intel, Tektro nix, Planar Systems, and InFocus cluster along the highways inBeaverton and Hillsboro.2 High-tech firms are also located in downtown Portland, inGresham, Oregon, east of Portland, and across the Columbia River in Camas and Vancouver, Washington. The seeds ofthat forest of high-tech activity were planted in 1946 with the founding of Tektronix, a company thatwould quickly become known as theworld's leading manufacturer of a specialized electronic test and measurement instrument called the oscilloscope. In the decades since Tektronix was founded, the Silicon Forest has matured and diversified. In the late 1970s, for example, Intel ? a de signer and manufacturer ofmicrocomputer components ? established itsfirstbranch plant outside SiliconValley in thePortland suburb of 568 OHQ vol. 106, no. 4 ? 2005 Oregon Historical Society Unless otherwisenoted, all photos reprinted with permission from Tektronix,Inc. \-_- -H-^-UK-"*-C Tektronix manufactured many parts of itsproducts in-house, including transformers, cathode ray tubes, and resistors, which thewomen on the right assembled at the Sunset plant in Beaverton. Mayer, Planting High-Technology Seeds 569 Aloha. In the 1980s, foreign high-tech firms ? primarily based in Japan ? were attracted to the region and opened manufacturing facilities near Portland. At the same time, talented entrepreneurs who had worked for Tektronix started their own companies. In the 1990s, the high-tech industry expanded in the Silicon Forest, with significant investments by Portland-based semiconductor firms such as Intel, LSI Logic, Fujitsu, and Integrated Device Technology. Tektronix was the anchor for the cluster of firms in the Portland area, attracting talented employees and serving as an innovative and vibrant center for research and development. When the company downsized in the 1980s, it continued its influence by acting as the incubator for the entrepreneurs who established the startup firms that kept the Silicon Forest healthy. Silicon Forest History The history of the Silicon Forest can be divided into two phases. The first, which lasted from the late 1940s to the 1970s,was dominated by Tektronix. A handful of other high-tech firms ? such as Electro Scientific Industries (originally Brown Engineering) ? had moved their offices to Portland, but Tektronix was by far the largest high-technology employer in the re gion. During the second phase of the Silicon Forest's development, from the 1980s to the present, Tektronix underwent significant restructuring, divesting itsbusiness units, closing its centralized research and develop ment (R&D) laboratory, and downsizing its labor force. In the early 1980s, the company employed more than fifteen thousand people locally; today, it employs only about forty-five hundred people worldwide. The changes atTektronix reverberated throughout the Silicon Forest, as the company's divested business units continued to operate as successful independent companies and former employees founded new companies. The knowledge that had been created at Tektronix's R&D laboratory was translated into new business opportunities for startup companies such as Planar Systems and TriQuint Semiconductors, which contributed to the region's competitive advantage in electronics research and manufactur ing. Today, these companies are leaders in theirmarkets and constitute the region's distinctive specialization in semiconductor production, test and measurement instruments, display technology, and electronic design automation. It is in this second phase that the diversity and strengths of the Silicon Forest were nurtured. In the early years, there were two prominent centers of high-tech in dustry in the nation ? the Silicon Valley and Boston. Both places gathered 570 OHQ vol. 106...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0370.004

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.

Opus teacher head0.031
GPT teacher head0.327
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

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