Counter-Improvised Explosive Devices: Multiple DOD Organizations are Developing Numerous Initiatives
Bibliographic record
Abstract
Correspondence issued by the Government Accountability Office with an abstract that begins "We identified 1,340 potential, separate initiatives that DOD funded from fiscal year 2008 through the first quarter of fiscal year 2012 that, in DOD officials opinion, met the above definition for C-IED initiatives. We relied on our survey, in part, to determine this number because DOD has not determined, and does not have a ready means for determining, the universe of C-IED initiatives. Of the 1,340 initiatives, we received detailed survey responses confirming that 711 initiatives met our C-IED definition. Of the remaining 629 initiatives for which we did not receive survey responses, 481 were JIEDDO initiatives. JIEDDO officials attribute their low survey returns for reasons including that C-IED initiatives are currently not fully identified, catalogued, and retrievable; however, they expect updates to their information technology system will correct this deficiency. Our survey also identified 45 different organizations that DOD is funding to undertake these 1,340 identified initiatives. Some of these organizations receive JIEDDO funding while others receive other DOD funding. We documented $4.8 billion of DOD funds expended in fiscal year 2011 in support of C-IED initiatives, but this amount is understated because we did not receive survey data confirming DOD funding for all initiatives. As an example, at least 94 of the 711 responses did not include funding amounts for associated C-IED initiatives. Further, the DOD agency with the greatest number of C-IED initiatives identifiedJIEDDOdid not return surveys for 81 percent of its initiatives."
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".