COTS acquisition process: incorporating business factors in COTS vendor evaluation taxonomy
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.
Bibliographic record
Abstract
The increasingly prevalent use of COTS components has attracted a huge capital pool to the industry. The result is an industry that is characterized by strong change forces and weak resistance. Under such environment, weaker players are constantly replaced by stronger players, and older technologies are constantly replaced by emerging technologies. This phenomenon has brought about a new class of risk to the COTS acquirers. These risk factors include the vendor's financial stability and technology capability. However, the existing COTS vendor evaluation taxonomies remain product centric, focusing only on product functionality and costs. We extend the taxonomies to incorporate business factors in the vendor evaluation process, and the resulting process is called VERPRO. The VERPRO decision making tool, which is based on the analytic hierarchy process, allows the acquirers to incorporate vendor business factors into the selection criteria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it