Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it