Bibliographic record
Abstract
CPFF Incentives and Their ImportanceOffi ce (GAO ) to be us ed as supporti ng docum entation and , generally, dir ected himself to the ov erall goal of co st savings .As a result of McNamara 's efforts , broad cha nges in Defense procur em ent polici es and procedures were begun in 1961 and have been applied decisiv ely in the past fi ve years. 2 The general procurem ent and logi stics di r ectives outli ned by Mr .McNam ara and the policy and proced ur al recommendations that followed from hi s chi ef procur em ent advi sor, Assistant Secr etary of Defens e (Installations and Logistics) Thomas D. Mo rris, have probed the business side of national defens e from both the industr y and the Department of Defens e (DOD) viewpoints .Seeking the best defense for the best price , DOD ha s looked critically into its own operation to promote new effici enci es.A parallel effort is aimed at removi
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".