A Silent War: Conflict Resolution and Language Policy and Planning in the North of Ireland and Quebec / Canada
Bibliographic record
Abstract
The theme of this book is the impact of conflict and conflict resolution (CR) on language policy and planning (LPP). To illustrate this interaction, I shall examine recent developments in the North of Ireland (NoI), with linking references to LPP in the Republic of Ireland (RoI) and in Quebec / Canada (Q /C). My primary focus is the Irish language in the NoI from the 1998 Good Friday Agreement (GFA) to the present (with brief contextual references where relevant to LPP in the Republic of Ireland [RoI]). LPP in respect of the Northern conflict has been neglected in both sociolinguistic and CR analysis. The book seeks to address this gap and also to place both CR and LPP in the NoI in an international context, drawing on Q / C as a mirror, through an examination of developments in federal — Quebec relations since the 1995 referendum on Quebec secession. There are some obvious similarities between the NoI and Q / C, as well as distinct and significant differences. It is not my intention to make forced comparison for the sake of illusory symmetry. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".