Making Performance Real: Six Paths to Training That Matters
Bibliographic record
Abstract
Training matters not only for business growth but also for talent acquisition and employee retention. Many experts and researchers highlight the importance and benefits of employee learning and development (Salas et al., 2012). The ATD (Association of Talent Development) 2019 State of the Industry and Training Magazine’s 2019 Training Industry Report indicates that billions of dollars and a tremendous amount of time are being spent on training. Many companies are concerned about the value of their current training programs, especially their leadership development programs (Deloitte, 2018; Kirkpatrick & Kirkpatrick, 2018; Beer et al., 2016; Bernal & Schuller, 2016). As we are experiencing a rapid digital transformation and tough economic times, companies are questioning the effectiveness of their leadership development models. This paper, first, aims to examine seven issues in the learning industry that lead to ineffective training from a practitioner’s point of a view. Then it discusses the Peterson, Song, and Udell (PSU) Training Model, an organizational talent development framework consisting of six specific, focused paths. We also focus on our 4E Training Design Model that resolves issues and makes performance real based on evidence from scientific research and insights from our experiences.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".