An Application of DEFINITE: the Quality of Life of Chinese Seniors in Four Districts of Toronto
Bibliographic record
Abstract
In this paper we demonstrate a new multi-criteria decision making (MCDM) Windows-based software package called DEFINITE (DEcision on a FINITE set of alternatives). Whereas MCDM techniques including DEFINITE are typically used to search for a preferred alternative from a set of options we offer another use. Specifically, we tackle a classification-type problem that comprises a small set of spatial units. We use a set of empirical data which comprises importance and achievement levels for a set of 14 criteria concerning quality of life (QOL) for a group of 80 Chinese seniors in four well-defined Chinese Settlement Areas in the Toronto CMA. For each area we have 20 responses. The conversion of scores for importance and achievement to QOL scores is explained. We show how DEFINITE can be used to classify the four areas in terms of QOL. A series of sensitivity tests is conducted and four separate multi-criteria techniques within DEFINITE are used namely. weighted summation, ELECTRE 2, Regime and Evamix. An informal evaluation of DEFINITE was conducted comparing it to other MCDM packages using opinions of senior undergraduates and graduate students and 10 evaluation criteria. Consistently DEFINITE is seen to be a superior package that deserves to be promoted and used for teaching and research purposes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".