Bibliographic record
Abstract
Today, most organizations need to extend lifelong learning opportunities to their employees in order to be successful in an increasingly competitive global marketplace. Organizations are turning to technological solutions to enable online in-house training and learning for their employees. An integrated approach to e-learning is important because it can be effectively used to analyze employee performance and also to gather information for continuous online and real-time learning of organizational goals to better tailor the educational product and its content. Online learning is made possible by advancements in network infrastructure and the development of voice and multimedia protocols for the seamless transport of information. E-learning involves encouraging the employee to spend time electronically to bring about learning, and to collect information and analyze it with respect to organizational needs, learning processes, and user preferences (Alavi & Leidner, 1999). E-learning ranges from simple computer use in a classroom where instructional materials are stored on a local-area network, to the use of simulation systems used to support teaching activities, or to distance education using broadband-enabled multimedia and shared electronic work spaces. E-learning styles include learner-centric, instructor-centric, and directed environments. E-learning communication modes include synchronous vs. asynchronous modes (time of interaction), and one-to-one, one-to-many, and many-to-many interaction modes. Presentation styles include voice only, voice and video, text only, text and animation, and voice, video, and text. Pedagogical approaches include objectivist, constructivist, and collaborative approaches and situated learning. Also, it is known that learning within organizations is affected by task complexity and the organizational environment (Argyris & Schon, 1996; Bhatt, 2002; Spender, 1996).
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.082 |
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".