Story Mapping Praxis to Principles: Learning from the Atlascine Project
Bibliographic record
Abstract
This article introduces and discusses story mapping principles inspired by the development of the latest version of Atlascine, an open-source, online platform for tagging, mapping and animating interviews and story collections. Shaped by critical cartographic theories and oral history research practices and ethics, Atlascine combines the analytical, visual and navigational strengths of maps with the immersive, evocative and emotional dimensions of stories. Through the design of this platform and the mapping of over 150 stories, we identified six core principles related to the process and purpose of story mapping that could inform a range of projects: (1) the map never replaces the story and the mapmaker never replaces the storyteller; (2) the integrity and totality of each mapped story are preserved; (3) the mapping process is made transparent; (4) the map offers a spatial synthesis of the stories while conveying their geographic complexity; (5) the map acts as a portal for listening to stories as well as for (6) a creative interpretation of story collections that provides an essential pluralistic reading of places. By embracing these principles, we reimagine the relationships between maps and stories, positioning mapping as a way to take care of stories and to engage with them for what they are rather than what can be extracted from them.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".