Bibliographic record
Abstract
Cartography has been pivotal in making visible the number of people who die in the context of migration. In this article, the author explores the potential of mapping to study and develop another dimension of the geography of death within exile: the more intimate dimensions of post-mortem geographies as experienced by those who survive a loved one. Inspired by Avril Maddrell’s call for developing new cartographic representations to share difficult emotions and memories associated with death, the author mobilized two alternative mapping practices—inductive visualization and sensibility mapping—to chart the emotional and intimate geographies embedded in the stories of two migrants who lost a close friend with whom they lived while in exile. The mapping process that led the author to represent these intimate post-mortem geographies brought me to reflect on the importance of developing alternative cartographic forms of expression that focus on the experiential and the emotional, rather than on the factual and the measurable. By steering this cartographic shift away from the fact of death as the end of a journey to death as a lingering event in the life of those who survive, the author proposes a cartography of grief and mourning that aims to contribute to individual and collective remembering.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".