Trends in spending on training: An analysis of the 1982 through 2008 Training Annual Industry Reports
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
This article explores long-term trends in spending using data compiled from the Training magazine Annual Industry Survey from 1982 through 2008. It builds on literature that proposes spending on training is an investment that yields benefits—and that offers methods for demonstrating it. After adjusting for inflation, aggregate spending on training rose 1.5% between 1986 and 2008. Inflation-adjusted spending on training staff fell 14%, although inflation-adjusted spending on outside products and services increased 237%. Spending on training fell in all but two job categories. Findings support the belief that spending on training is falling but suggest that this is a sustained and systemic drop rather than a temporary response to an economic crisis. Also, expenditures on internal training resources have fallen, while spending on external resources has risen. A key limitation of this study is that it relies solely on data from the Training Annual Industry Survey.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it