Bibliographic record
Abstract
This contribution starts with a discussion of various types of portfolios existing in literature. It then combines the buyer’s view, the seller ’ view and the complexity of the product. This results in a cube. As an example, one test case is provided to show the relevance and application of the cube. The paper goes on with adding the potential of E-Business (both E-Commerce and E-Porcurement) and the effects on the networks, the type of roles actors will play in the new situation a d the type of actors that will play these roles. This paper has been presented as a poster session at the IPSERA meeting in 1999 in London (Ontario). The concepts developed have since been tested on several companies. The paper follows the following line of arguments. 1. The classical portfolio matrix: the Kraljic matrix The portfolio matrix (Kraljic, 1983) is a useful tool to classify purchased goods and the suppliers involved. Taking the complexity of the supplier market and the financial relevance or impact into account, goods and suppliers can be classified as leverage, routine, strategic and bottleneck items. The strength of the instrument is that it enables the purchaser to differentiate between the various supplier relations and strategies that are appropriate for each category. These are
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.620 | 0.352 |
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; the direct Gemma label and the distilled Codex classifier 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".