Bibliographic record
Abstract
BACKGROUND AND ANALYSIS 1.Why Did the Adequacy of Active Duty Military Pay Become a Major Issue Beginning in the Late 1990s? 2. What Kinds of Increases in Military Pay and Benefits Have Been Considered or Used in the Past? 3. How Are Each Year's Increases in Military Pay Computed?Definitions Annual Percentage Increases in Military Basic Pay 4. What Have Been the Annual Percentage Increases in Active Duty Military Basic Pay Since 1993 (FY1994)?What Were Each Year's Major Executive and Legislative Branch Proposals and Actions on the Annual Percentage Increase in Military Basic Pay? 5. Is There a "Pay Gap" Between Military and Civilian Pay, So That Generally Military Pay Is Less than That of Comparable Civilians?If So, What Is the Extent of the "Gap"?Measuring and Confirming a "Gap" Estimates of a Military-Civilian Pay Gap If There Is a Pay Gap, Does It Necessarily Matter? 6.What Benefits Are Specifically Available For Military Personnel Participating in Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF -Service in Afghanistan)?Hostile Fire/Imminent Danger Pay Hardship Duty Pay Family Separation Allowance Per Diem Savings Deposit Program Combat Zone Tax Exclusion Rest and Recreation (R&R) for Personnel In OIF/OEF 7. What Cash Lump-Sum Death Benefits are Available to the Survivors of Military Personnel Killed in Iraq or Afghanistan, and What Increases in Such Benefits Have Been Proposed?
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.567 | 0.507 |
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".