Use of Project-Based Learning of Adults at Corporate Universities in The US and Canada
Bibliographic record
Abstract
Abstract Objective: The article defines features of formation and development of corporate universities in the USA and Canada. Methodology: The article analyzes the dependence of successful functioning of the corporate universities on the choice of adequate training technologies; explores the essence and potentials of project-based learning as action learning which is focused on personnel development, business development and effective management of changes. Findings: There is a close relationship between the performance of the functions of the corporate university and the forms, methods, learning technologies that are used in the learning process. Project-based learning is widely used in corporate universities in the United States and Canada; it provides an opportunity to gain managerial experience in real time, solves an important task of personnel development – formation of the ability to learn. Value Added: The results of the research give ground to conclude that the corporate university in the US and Canada is a structural unit of a company, which performs certain functions that promote business efficiency. Recommendations: The project topic should be related to current or future changes in the company. The solution of the problem should include diagnosing of the problem, analysis, recommendations, implementation phase, as also cooperation with members of the company.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".