A technology management methodology implemented using expert systems
Bibliographic record
Abstract
Management of technology concerns the processes of managing technology development through research and development, and managing the introduction and use of technology. Expert systems are computer programs that encode knowledge about a problem into a knowledge base, and then use this knowledge to reason and draw conclusions about a particular problem or situation. Currently, expert systems are being used in many areas, but we have found no examples of expert systems being applied in the field of technology management. The objective of the paper is to set out a methodology for technologies for implementation in a small business, using an expert system structure to automate the procedure. The expert system is intended to be used as a guide, providing a structured approach that will allow a consultant to provide consistent recommendations, as well as reducing repetition in evaluating the technologies for different situations. The expert system compares customer requirements to the attributes of available technologies, and suggests which technologies should be considered for implementation. The matrix structure of the Quality Functional Deployment ("House of Quality") approach to quality control is the basis for the expert system methodology presented.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".