Using Organizational Science Research to Address U.S. Federal Agencies’ Management & Labor Needs
Bibliographic record
Abstract
Employee performance often moves in lockstep with job satisfaction. Using the 2015 Federal Employee Viewpoint Survey we have identified important and common management and labor needs across more than 80 federal agencies. Drawing on the vast trove of organizational science research that examines the effects of organizational designs and processes on employees’ and organizations’ behaviors and outcomes, we offer specific evidence-based interventions for addressing employee dissatisfaction or uncertainty that breeds lackluster performance, managerial shortcomings, and needed supports. Our intervention and policy recommendations have the synergistic goals of improving employee well-being, employee productivity, agency performance, and agency innovation, all resulting in increased efficiency and effectiveness, which benefit the taxpayer. Our top recommendations directly target the goals of improving employee motivation through engagement, empowerment, and embeddedness; enhancing the employees’ voice; and fostering both internal and across-agency cooperation, communication, and collaboration. These recommendations are general enough to apply across diverse government agencies yet specific enough to yield results in discrete agency units.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".