Civilian Stabilization Team Leadership Success: Perceptions for Iraq Provincial Reconstruction Teams1
Bibliographic record
Abstract
Team Leaders were a crucial element of Iraq Provincial Reconstruction Teams (PRTs), and they dramatically impacted the performance of a team, both positively and negatively. This review examines PRT members’ perspectives of Team Leaders (TLs) and the roles leaders played in PRT successes, as well as the particular challenges that leaders faced. This review is based primarily upon interviews of PRT members ending their tours. Beginning in the latter half of 2009, the Center for Army Lessons Learned (CALL) and the Center for Complex Operations (CCO) conducted lessons learned interviews with departing Iraq PRT members. Over 100 interviews from this process were examined for this review (initially completed in October of 2010). Though derived from feedback on Iraqi operations, the examples and insights found here should also be applicable to other or future reconstruction and stabilization operations. It is likely that similar leadership challenges or roles will be required in similar circumstances, when operating in hostile conditions, and with teams composed of members with a number of different organizational perspectives and goals.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".