Geographies of Conflict and Post‐Conflict in Northern Ireland
Bibliographic record
Abstract
Abstract The violent conflict in Northern Ireland that led to some 3700 deaths was tied to opposing ethno‐sectarian groups and the state and the disputation between them over that country’s constitutional future. Republicans such as the Irish Republican Army used violence in order to ‘gain’ a united Ireland. Whereas loyalists such as the Ulster Volunteer Force and the British state utilised violence in order to maintain Northern Ireland’s constitutional link with the United Kingdom. Geographers writing on this conflict have, via various forms of spatial analysis studied the consequences of that violence with regard to territoriality, the construction of ideological space and the perpetuation of ethno‐sectarian boundaries. In more recent times, the analysis of conflict and post‐conflict Northern Ireland has evaluated the governance of a divided society, the role paramilitaries have played in embedding peace and also the paradoxical role of reproducing conflict by other non‐violent means. Northern Ireland remains as a divided society but there is a prominent role for geographers who study such a complex place to add to wider international scholarship regarding resistance and domination, revanchism, discourse construction, post‐conflict and the impact upon place and also the role of agency in both perpetuating and removing 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".