Bibliographic record
Abstract
This article sets out to catalogue narration strategies used in the professional discourse about Effects-Based Operations (EBO). EBO was at the heart of the US military transformation (2001-2008) and is one of few concepts officially discontinued instead of being simply replaced by a successor concept making it a crucial case for analysing its rise and fall. An analytical framework for classifying the rhetoric of military innovations is presented in this article. Based on this framework the debate about EBO in the U.S. military journal Joint Force Quarterly between 1996 and 2015 is assessed with a view to three questions: How was EBO framed by military experts? Was the shift of institutional support for EBO reflected in the discourse? And, is there evidence to suggest that the EBO discourse had an influence on the adoption and later discontinuation of EBO? The analysis shows that in the case of EBO a particularly homogenous discourse pattern existed, which might have contributed to the concept’s quick and ultimate demise.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".