Narratives of Memory, Migration, and Xenophobia in the European Union and Canada
Bibliographic record
Abstract
The chapters presented explore how varied agents of memory -- including the music we listen to, the (his)stories that we tell, and the political and social actions that we engage in -- create narratives of the past that critically contest and challenge xenophobic and nationalistic renderings of political possibilities for Europe and Canada. The goal of the volume as a whole is to foster innovative interdisciplinary and intercultural discussions on memory discourses as political, social, and creative collective ventures. In addition, the volume aims to contribute to the development of curriculum geared towards exploring the politics of memory in shaping present-day tensions and conflicts. Specific themes explored in the volume include: \n \n1)\t How memory politics and narratives of the past frame and influence current political decisions and decision-making processes \n2)\tHow memory discourses and narratives of the past can be deployed as agents of change and resistance to destabilizing and fracturing discourses \n3)\tHow cultural narratives of the past and memorialization are interwoven with current public policy challenges relating to multiculturalism and diversity \n4)\t The role of integrative national and transnational identities in the face of rising nationalism and xenophobic discourses
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.044 | 0.033 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".