How Distant is Close Enough? Exploring the Toponymic Distortions of Life Story Geographies
Bibliographic record
Abstract
Stories are now broadly recognized as important sources of geographic information in different domains of the spatial humanities. The methodologies mobilized to identify these spatial data, however, remain the subject of intense debate. In this paper, we contribute to this debate by focusing on what we can learn from the close reading of stories to improve the quality of distant reading approaches. We do this through an in-depth comparative analysis of how toponyms are used across 10 oral life stories of exiles. Results show that a “distant listening” of the number of country names mentioned in these stories provides an accurate representation of their global geographies. However, the finer-scaled geographies of these stories become highly distorted when counting more local toponyms such as neighborhoods, cities or regions. This study also reveals that results could be improved by accounting for the distribution and repetition of toponyms throughout these stories. Such insights and their nuances are described in this paper with an aim to help narrow the gap between close and distant reading methodologies.
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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 | 0.012 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".