From Belfast to the Somme (and back again): loyalist paramilitaries, political song, and reverberations of violence
Bibliographic record
Abstract
During the Northern Ireland conflict (1968–1998), paramilitary groups were supported and sustained by a sociocultural apparatus that helped legitimise their position within the community and disseminate their political message. From the use of flags and murals, to loyalist and republican parades, working-class vernacular culture revealed who was in control of various districts within the Province. For many working-class Protestants, loyalist songs were a key component of this culture, connecting the past and the present. Unlike the better-known marching band scene, which is a huge public spectacle, the loyalist song scene is much more private. Performed in a closed setting, within local bars and clubs, loyalist songs are reproduced for internal consumption rather than outward expression. Yet, in addition to celebrating a particular loyalist culture, such songs also serve an important function in authenticating and legitimising paramilitary groups, connecting them to older organisations, whose legacy they draw upon. This paper focuses on one such song, exploring how ‘The Ballad of Billy McFadzean’ is used to connect the Ulster Volunteer Force of the 1960s onwards, with the 1913 organisation of the same name. In so doing, the paper attempts to illustrate the political utility of song and how songs can be used to launder and legitimise conflict, as well as those engaged in political violence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".