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Record W2086812557 · doi:10.1002/piq.20092

Trends in spending on training: An analysis of the 1982 through 2008 Training Annual Industry Reports

2010· article· en· W2086812557 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePerformance Improvement Quarterly · 2010
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsConcordia University
Fundersnot available
KeywordsTraining (meteorology)Inflation (cosmology)FellInvestment (military)EconomicsFalling (accident)Survey data collectionDemographic economicsBusinessLabour economicsPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.060
GPT teacher head0.351
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it