An analysis of fiscal years 2014 to 2016 Navy Fourth Quarter spending: trends and characteristics of Q4 O&M contractual awards
Bibliographic record
Abstract
Former United States Under Secretary of Defense Robert Hale stated in a September 2016 article for Breaking Defense, We need to find practical ways to apply the brakes to year-end spending so that [the Department of Defense] funds only its highest-priority needs. This paper analyzes trends and characteristics of the Quarter 4 (Q4) Navy spending habits driving the government’s decisions and the related impacts of those decisions. Previous trends have shown that under-execution in the government leads to future funding decrements. Although seldom documented, this practice leads to increased late spending and a potential for executing ahead of need, but results in an obligation of funds. Our research identifies trends across contractual spending in the Navy Operations and Maintenance accounts between fiscal years 2014 and 2016 to help ensure the government is getting the best value for the limited resources available. Analysis indicated that actual Q4 spending appears higher than historical rates, in excess of 35% in all years. We also noted trends in Q4 spending leading to an increased level of Indefinite Delivery Contracts, and a significant increase in overall contract actions processed. Surprisingly, even with the rush to obligate, 2014 data showed Q4 obligations trended higher than average utilizing Full and Open Competition.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".