Of Garbage Cans and Paradox: Reflexively Reviewing Design, Mission Command, and the Gray Zone
Bibliographic record
Abstract
As a rule-following organization, the military both suffers and benefits from bureaucracy. One of the negative characteristics of bureaucracy is the co-opting of innovation. This co-option results in the dismantling of ideas and the re-wickering of innovative tools into something unworkable or at cross-purposes to their original intent. To make sense of this we first must look at the concept of paradox and understand that what we observe is not irrational or abnormal: paradox is the rule in how human institutions behave. Second, there are ways with which to make sense of paradox. Next, one example is provided, applying the Garbage Can Model of Decision Making to some of the military’s examples of paradox. Lastly, I use these insights to describe how organizations co-opt new ideas. This concept could allow military professionals to understand what happens to new ideas and why they happen so that they can anticipate co-option’s negative effects.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".