The measurement of humanware readiness in a technology transfer process: Case study in an electrical machinery company
Bibliographic record
Abstract
The increasing competition in electric machinery industries makes METALCO, as one producer of electric machinery in Indonesia, has to develop its products in order to be able to win the competition. This product development has forced METALCO to implement a technology transfer process. The success of a technology transfer process mostly depends on the readiness of its humanware, as is is the crucial element of technology transfer. For this purpose METALCO has to identify its humanware readiness. The model was developed using a Delphi Method. The measurement model developed consists of 6 criteria and 19 sub-criteria. The criteria used is based on the generic model of humanware sophistication level of UNESCAP (1989); and the sub-criteria are developed based on the competency model of Spencer & Spencer (1993), Georgia Merit System (2005), and the University of Guelph (2010). Result of the measurement showed that in general the humanware of METALCO's Electric Machinery Department is in Phase II, with an average readiness score of 3.203. It is also discovered that in the sub-criteria integrity, the humanware have not reached the agreed assessment degree for the second phase of technology transfer. The gap in this sub-criteria is then used as a base in developing a competency enhancement program for humanware in the technology transfer process. The program proposed is a mentoring program based specifically related to job integrity criteria.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".