How Distant is Close Enough? Exploring the Toponymic Distortions of Life Story Geographies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it