Bibliographic record
Abstract
Three factors have accelerated the need for continuous learning for public administration employees: (1) improvements in information technologies that provide greater opportunities to gather, store, and transmit knowledge; (2) the increase in jobs required to produce and to manipulate knowledge; for example, the Canadian federal government estimates that 75% of its employees are knowledge workers; and (3) employee mobility is increasing, as shown by a yearly turnover of 40% of the U.S. workforce, or approximately 50 million employees. Therefore, continuous learning is becoming an important issue for employers and employees. In a survey of the 50 U.S. state governors on higher education issues, the most important issue was encouraging lifelong learning (Klor de Alva, 2000), leading to a need for learning management, where an organization controls internal and external knowledge as an important performance factor for both individuals and the organization. In the past, continuous organization learning in the public sector was associated with dedicated central learning centers. For example, the Canadian federal government’s education center for middle- and senior-level public servants, the Canada School for Public Service, had two large centers with classrooms and libraries, one with residential capacity. Most of the new knowledge obtained by public servants required large expenditures of capital and time in order to move employees and instructors away from their places of work to learn together in classrooms. Not only is this model expensive, but it also places a significant gap between learning a concept and being able to apply that concept to daily work. Online gives the learning manager a new tool that might be more cost effective (Langford & Seaborne, 2003). With the development of new information technologies, many leaders are questioning the place-bound synchronous classroom model as the best model for developing educational experiences. For example, U.S. governors’ next three important higher education issues after lifelong learning were (1) providing opportunities to obtain education anytime and anyplace via technology, (2) requiring postsecondary institutions to collaborate with business and industry in curriculum and program development, and (3) integrating on-the-job experience into academic programs (Klor de Alva, 2000). The new instructional model that is emerging delivers smaller units directly to the employee and very close to their work site or home; it is often called online education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.343 | 0.144 |
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".