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Technology planning approach for Very Small Entities

2016· article· en· W2519644544 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINCOSE International Symposium · 2016
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsProcess (computing)Process managementDomain (mathematical analysis)New product developmentProduct (mathematics)BusinessComputer scienceEngineering managementKnowledge managementEngineeringMarketing

Abstract

fetched live from OpenAlex

Abstract Systems engineering is usually seen as the domain of large enterprises. However, small and medium sized (SMEs) and micro‐enterprises are coming to play an ever‐larger role even in industries traditionally dominated by large enterprises. In new product development projects carried out by SMEs or micro‐enterprise using systems engineering, three aspects should be noted. Firstly, such enterprise wants to initiate the project independently, i.e. develop a new product/system under ISO 29110, there is some information regarding how it could do it, i.e. the stage that would involve the definition of the requirements. But enterprise should be able to understand and apply this information. Secondly, it must be underlined that project execution, is preceded by establishment of cooperation of new product development process. This process is frequently tedious, time‐consuming and – especially for small organizations – troublesome. Thirdly, such cooperation should be anchored in the strategy especially in technology strategy of the company. Technology roadmapping could be considered as a tool which is helpful in technology strategy creation process.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.231
Teacher spread0.219 · 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