Web-Based Competency and Training Management Systems for Distance Learning
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
The Web has had a major impact on how corporate training departments manage employee training. The evolution of computers and networks allows companies to implement a precise customer-focused approach. Through the use of competency and training management systems such as the SIGAL system used by Bell Canada, organizational training plans can be efficiently communicated throughout the organization, training needs can be linked to the performance evaluations of individual employees, and online training materials can be conveniently delivered to employees at their desktops. In the future, we predict that training management systems will evolve to incorporate analytic tools that can calculate the return on training investment, evaluate the impact of training on job performance, and determine the impact of training on corporate profits. This chapter discusses the value to companies of using a Web-based system for competency and training management, using the case of Bell Canada as an example of how companies are implementing these tools today.Request access from your librarian to read this chapter's full text.
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