Narrative Cartography: From Mapping Stories to the Narrative of Maps and Mapping
Bibliographic record
Abstract
This paper provides an overview of the multiple ways of envisioning the relationships between maps and narratives. This is approached from a map making perspective. Throughout the process of editing this special issue, we have identified two main types of relationships. Firstly, maps have been used to represent the spatio-temporal structures of stories and their relationships with places. Oral, written and audio-visual stories have been mapped extensively. They raise some common cartographic challenges, such as improving the spatial expression of time, emotions, ambiguity, connotation, as well as the mixing of personal and global scales, real and fictional places, dream and reality, joy and pain. Secondly, the potential of maps as narratives and the importance of connecting the map with the complete mapping process through narratives is addressed. Although the potential of maps to tell stories has already been widely acknowledged, we emphasize the increasing recognition of the importance of developing narratives that critically describe the cartographic process and context in which maps unfold - the core idea of post-representational cartography. Telling the story about how maps are created and how they come to life in a broad social context and in the hands of their users has become a new challenge for mapmakers.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".