The Politics and Ethics of Collective Memory and Forgetting in Christina Reid’s <i>My Name, Shall I Tell You My Name?</i>
Bibliographic record
Abstract
This article explores the relationships between personal and collective memory, especially transgenerational memory, within a Protestant, loyalist family in Northern Ireland in Christina Reid’s My Name, Shall I Tell You My Name?. Forgetting plays a vital role in the communal memory of loyalism and unionism within the world of the play. The female protagonist, Andrea, actively unsettles that forgetting by challenging the mainstream loyalist commemoration exemplified by her grandfather Andy via alternative narratives and commemoration. The ongoing contestation over the collective memory of the Battle of the Somme, one of the pivotal historical events in loyalist remembrance culture, reveals the peculiar temporality of loyalist memory and uncovers problems inherent to the eternal cycle of loyalist memory and its oblivion. Through its treatment of these themes, I suggest that My Name conveys an ethical imperative to remember for the future instead of the past.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.070 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".