‘Spatializing’ Travel Narratives in the Belgrade Forest Project: Grounded Methods and Reflexive Strategies for Interdisciplinary Collaboration
Bibliographic record
Abstract
The Belgrade Forest Project explores the history of a 5,550-ha forest in the northern suburbs of European Istanbul. The first stage of the project involved creating a reproducible strategy for extracting place names and their geographic locations from 100+ travel narratives that reference the forest area. In this article, we share the initial outcomes of a collaboration between researchers in forestry, geography, and linguistics, as well as a documented methodology, coding protocol, and an open, reusable, and interoperable research dataset of tagged place names. We describe a grounded, pragmatic, reflexive, iterative and communicative approach for interdisciplinary research in spatial humanities. Future project stages will enhance the narrative collection with data from other archival materials, such as maps and historical photographs. Outputs will include a multilingual, geolocated data collection of historical landscape features, places, and agents. The results of the project will enhance our understanding of the forest’s historic and ongoing significance to the region.
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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 | 0.074 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".