Bibliographic record
Abstract
For years, the Survey of Employment, Payrolls and Hours (SEPH) Section of Statistics Canada’s Labour Statistics Division has been using UNIX servers to meet the needs of its surveys and its clients. Because of the complexity of processing the data, using SAS on a UNIX server was considered to be the best choice to speed up data manipulation. To date, SAS/AF ® has been used to develop the editing and process management graphical user interfaces (GUIs). Over the years, however, we have received increasingly complex requests for data analysis and editing GUIs. Today, the task of providing our clients with high-quality products that satisfy their requirements is even more challenging. The UNIX version of SAS/AF offers a very limited set of tools for building complex GUIs. Consequently, other ways of meeting the Division’s operational requirements have been explored. The PC version of SAS/AF was tested, and although it is more flexible than the UNIX version, it too has limited capabilities. SAS IT (Integration Technologies) together with Microsoft Visual Basic.Net (VB.Net) was also tested. This appears to be the best solution since it is capable of developing complex GUIs in a reasonable amount of time. In addition, VB.Net is much more widely used than SAS/AF, and it is much easier to find reliable resources for building GUIs. This article focuses on two separate problems. First, we will look at integration, using a concrete example from our research in both SAS IT – specifically, the Integrated Object Model (IOM) server and the SAS/CONNECT ® server – and Microsoft Visual Basic.Net. Second, we will propose a solution combining Visual Basic.Net and SAS to solve the problem of navigating large files (files with millions of data elements) in real time.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.210 | 0.137 |
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".