Bibliographic record
Abstract
All warfare affects children, but there is mounting evidence that current conflicts are having a greater impact than ever before … African communities are increasingly … permeated by forces, relations and pressures … integrally related to globalization … Constrained by debt and structural adjustment programmes the economies of many developing countries have undergone restructuring which has involved cutting basic services and reducing the size of the public sector. Inequalities have widened and the effect on the social fabric has been to make livelihoods more insecure. Household and community capacities to nurture and protect are declining and there has been a weakening of the norms to protect children … This has resulted in the commodification of children … which is inducing an increase in child labour, including child soldiering. (Maxted 2003, pp. 51–2, 65) Many under-age combatants choose to fight with their eyes open and defend their choice, sometimes proudly … [M]ilitia activity offers young people a chance to make their way in the world … As rational human actors, they have [a] surprisingly mature understanding of their predicament. (Peters and Richards 1998, p. 183) While international concern and scholarly attention has increased about child soldiers and their seeming omnipresence within armed groups and forces, for the most part, children's involvement in armed conflict has tended to be explained by largely contrastive viewpoints.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".