Deep Maps, Authorship, and Narrativizing Physical Spaces
Bibliographic record
Abstract
In this paper, we propose that “deep mapping,” as described by David Bodenhamer (Bodenhamer 2016), can be used as a technique to broaden the scope of who is allowed to tell stories about place by letting a map’s authors re-engage physical spaces through narrative, subverting the notion of a map as an authoritative representation of place. We introduce The MapTool, a custom software toolkit we have developed in collaboration with authors telling stories about places. This tool facilitates the creation of interactive maps for both web and mobile applications. By presenting three case studies of projects composed using The MapTool, we examine ways in which deep maps provide an opportunity to co-construct stories of physical places through layering.Dans cet article, nous proposons que la cartographie profonde, telle que décrite par David Bodenhamer (Bodenhamer 2016), puisse être utilisée comme technique pour élargir le champ des personnes autorisées à raconter des histoires sur les lieux en permettant aux auteurs d'une carte de réengager les espaces physiques par le biais de la narration, en subvertissant la notion de carte en tant que représentation autoritaire d'un lieu. Nous présentons The MapTool, un logiciel personnalisé que nous avons développé en collaboration avec des auteurs qui racontent des histoires sur les lieux. Cet outil facilite la création de cartes interactives pour les applications web et mobiles. En présentant trois études de cas de projets composés à l'aide de The MapTool, nous examinons comment les cartes profondes permettent de co-construire des histoires de lieux physiques par la superposition de couches.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".