Technology planning approach for Very Small Entities
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".