Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA
Bibliographic record
Abstract
Pensions are fundamental to financial security in retirement, especially in the U.S., where they play a critical role in ensuring stability for retirees and fostering broader economic benefits. However, predicting pension coverage trends poses significant challenges due to the complexity of labor markets, demographic shifts, and economic variabilities. Traditional statistical models, though foundational, often fail to handle the nonlinear patterns inherent in pension data. To address these limitations, we propose the Inception residual RNN-LSTM hybrid model, which combines the strengths of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks with residual connections. This model captures diverse temporal patterns while mitigating vanishing gradient issues, delivering superior performance in predicting pension coverage trends. Experimental results demonstrate that our model outperforms traditional machine learning models and standalone deep learning architectures like RNN and LSTM. Its robust performance across key metrics highlights its potential as a reliable tool for forecasting complex pension trends and aiding policymakers, employers, and financial institutions in effective retirement planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".