Mapping-Ofrenda: mapping as mourning in the context of migration
Bibliographic record
Abstract
This paper proposes the concept of mapping-ofrenda, which envisions mapping as a form of mourning and remembering while living in the context of migration. Inspired by the traditional Mexican ofrenda, the mapping-ofrenda aims to collect, curate, and represent posthumous memories. It can be produced collaboratively or individually, built with physical or digital maps, shared with other people, or kept private, and be dedicated to a single deceased or to an entire community. Through the process of co-designing two online ofrenda-maps with two Latina-American women living in Montreal (Canada) we identified some of the potential of mapping-ofrenda, including its capacity to stimulate our memories and remember stories on the verge of disappearing, to ground them to places, and to share them with people that might live far away. Mapping-ofrenda can also be a way of making visible the global geography of migration through highly intimate memories and acknowledging both the very personal and the highly universal need to remember and grieve. Finally, the main value of mapping-ofrenda in the context of migration, may be its capacity to reactivate and strengthen existing links and connections between people that are still alive but that may live far away.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".