Instilling the Spirit - Learning Strategies for the New Millennium: The Bachelor of Education in Enterprise Education Program
Bibliographic record
Abstract
The mental models, learning styles and world views that people internalized in last century's industrial era no longer serve the demographic, economic, environmental, and social needs of the 21st Century. New learning methodologies and strategies are needed to connect each individual's distinct essence of being with emerging opportunities in today's highly disruptive environment The Institute for Enterprise Education (IEE) has developed such a curriculum that seeks to connect the learner and facilitator with learning opportunities that enhance their capability and connection with emerging opportunities in the external environment This paper. provides a global context for the need to instill the entrepreneurial spirit into every subject field of the educational spectrum; evaluates entrepreneurship as an effective process for interacting with today's highly disruptive global environment; identifies scientific paradigms that provide a systems approach to understand the new rules and the nature of interaction (Science of Complexity); synthesizes the theory behind complexity sciences and the practice of entrepreneurship to provide a learning strategy for each individual (Human Factor); develops an evolutionary path for the Bachelor of Education in Enterprise Education program for new student teachers, a symbiotic partnership between IEE and Brock University's Faculty of 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".