Bibliographic record
Abstract
This essay uses the SWORD model developed by Max Manwaring to analyze the ongoing insurgency in Iraq and assess the coalition efforts in rebuilding the country, fighting the insurgency and transferring authority to an Iraqi government. This essay was written in late February 2004. The essay looks at the current situation in Iraq and uses a graphical ‘conflict mapping’ technique developed by the Canadian Pearson Peacekeeping Centre as a visual tool to portray the principal actors in Iraq and their interrelationships. Next, the author applies the seven dimensions of the SWORD model to the on-going counter-insurgency campaign in Iraq. The seven dimensions of the SWORD model are (1) military actions of the intervening power, (2) support actions of the intervening power, (3) host government legitimacy, (4) degree of outside support to insurgents, (5) actions against subversion, (6) host country military actions, and (7) unity of effort. The model suggests that coalition efforts are hampered by a lack of host government legitimacy, inability to limit outside support to the insurgents, weak host country military actions, and lack of unity of effort at various levels. Although this essay does not offer a prediction of the coalition efforts or for the future of Iraq; it does provide some possible lessons learned that may improve the prospects for success in the future of Iraq.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".