A UML Model of the Client Tracking System at the Learning Enrichment Foundation in Toronto, Canada: A Study of System Specifications and Use Case Diagrams
Bibliographic record
Abstract
This study attempts to apply UML concepts to design UML diagrams that reflect the functional processes within the Client Tracking System (CTS) of a community service organisation. This paper represents Part I of a case study of the UML model for designing the CTS at the Learning Enrichment Foundation (LEF) in Toronto, Canada by Tran (2007). It investigates the task of constructing the design elements for CTS that can be used to manage the client information within LEF. Specifically, this paper investigates the system requirements in association with business and user needs, and use case diagrams of CTS. Furthermore, this investigation represents the UML model of the existing CTS at LEF. Through a study of how the CTS has been structured and how it operates, we may learn lessons that may be useful in other community networks serving significant immigrant communities.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".