A Comprehensive Analysis of the Indicators of Training Effectiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The past 55 years of training research and practice has been stunted by Kirkpatrick’s memorable but atheoretical framework, which proposes that training evaluation should progressively advance through four levels. To elevate the science underlying evaluation efforts, we propose a multilevel framework that addresses the criteria that can be used to assess training effectiveness at the within-person, between-person, and organizational levels of analysis. Specifically, we propose four evaluation targets--training utilization, affective effectiveness indicators, performance effectiveness indicators, and the cost effectiveness of training--as well as the specific evaluation metrics that can be captured to examine each target. Our multilevel framework also clarifies the appropriate level of analysis for assessing each criterion variable and articulates when it appropriate to aggregate responses from a lower level of analysis to assess training effectiveness at a higher level of analysis. Finally, we illustrate how training evaluation criteria are interrelated because understanding constructs’ nomological network is essential for gauging the depth of knowledge that can be inferred by any evaluation effort.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it