Curriculum Development for Business and Industry
Bibliographic record
Abstract
Curriculum development is usually associated with educational institutions. As a result, there are few curriculum development models that have been specifically created for the business and industrial setting. Those that have been published tend to adopt a “let's begin at the beginning” approach. They prescribe starting as though nothing previously existed within the organization to provide personnel training and development. The Professional Development Curriculum (PDC) model presented in this article starts with what already exists organizationally. It adopts a convergence strategy. It begins by systematically matching known needs with known resources and then, over a series of generations, creates closer fits between needs and resources. The model has been applied to two very different settings in General Motors: all GM wholesale divisions and GM's Latin American retail and wholesale operations. The results have been positive in creating coherent curricula tied to career path progressions for all employees in these organizations. Evolutionary and practical, this PDC model can be applied to any business or industry to build competency-based curricula that not only provide personnel development support systems for today's needs, but for tomorrow's as well.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.017 |
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".