Bibliographic record
Abstract
We discuss in this paper the decision making in choosing the best alternative from some available options based on possibly a large number of selection criteria. This multi-criteria decision problem typically arises in supplier selection in supply chain management. Recently, there has been an increasing interest in the applications of dimensional reduction methods such as factor analysis to such decision processes. They have been widely applied in conjunction with some classical methods such as AHP to create a hierarchical structure and identify the underlying factors or constructs. There are, however, a number of inherent issues and difficulties which have not been adequately addressed in the literature. For instance, there may be some criteria which load significantly on more than one factor, creating considerable difficulties in categorizing the criteria into mutually exclusive groups. More importantly, it is seen in this paper that it is not always sensible to determine the importance of an identified factor according to its amount of shared common variance or explained variation. Similarly, attempts to routinely determine the local relative weight (within a factor) of importance of a criterion based on its factor loading or correlation with the factor may also lead to results markedly different from those based on the views or judgement of the practitioner or expert. To circumvent these difficulties, a simple, practical and easily implemented procedure is proposed. Although factor analysis is employed, it merely serves as a means of facilitating the direct rating of importance of each criterion, alleviating many of the difficulties of the classical factor analysis approach. Two examples are given to illustrate the proposed method and illustrate some potential problems of current approaches in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".