Bibliographic record
Abstract
This second issue of The EPS Journal takes up the theme of economic aspects of peacemaking and peacekeeping. Economics Nobel-Laureate Lawrence R. Klein reviews the arguments for, and the likely cost of, a standing United Nations peacekeeping force. Lloyd J. Dumas argues that minimizing economic stress points also helps minimize the potential for conflict, and Dietrich Fischer reviews the cost of war as against the cost of war-prevention. But for all the good reasons of why peace is cheaper than war, war nonetheless recurs. Jurgen Brauer examines why there seems to be so little peace - if it is so cheap to obtain - and studies the conditions under which states appear willing to intervene in trouble spots elsewhere. Bassam Yousif, Guy Lamb, J. Paul Dunne, and Ross Fetterly, present a set of country studies - on Iraq, Namibia, Mozambique, Rwanda, and Canada. The Canadian piece is of particular value as there is virtually no literature in existence that tries, as Fetterly does, to compute the cost of providing peacekeeping services. The other country studies offer valuable comparative lessons of what does, and does not, work in post- conflict reconstruction. The final two articles look at the business side of things. Bob French has written a forceful account of what it takes to clean up land mine pollution, and John T. Marlin examines what consumer campaigns might do, and have done, to rattle the market for gold jewelry - and thereby compel gold-mining companies to adopt behaviors that might reduce conflict.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".