Maps, volunteered geographic information (VGI) and the spatio-discursive construction of nature
Bibliographic record
Abstract
This paper interrogates the role that spatial media such as maps and Volunteered Geographic Information (VGI) play in the construction and mobilisation of representations of nature. Drawing on poststructural political ecology, critical cartography, and GIScience, this article engages maps and VGI as discursive mechanisms that solidify and convey meanings and representations of nature tied to broader strategies of commodification. Particularly, we explore how spatial media reproduces and legitimises discursive strategies that rationalise the reconciliation of economic development and conservation through nature-based tourism by producing new ways of nature commodification. Drawing on evidence from Patagonia-Aysén, Chile, this paper examines the intersections between the discourse of nature encoded within institutional tourist maps and advertisements, and within the VGI platform for travellers, TripAdvisor. This illustrative case shows, firstly, how tourist maps and advertisements have contributed to normalising a discursive construction of nature as pristine, grandiose, sublime and wild that has not only secured aesthetics as ontological qualities of nature, but also as embedded values that protect ‘nature’ as a commodity to consume. Secondly, our findings evidence that TripAdvisor emerges out of this context as content that mobilises individual perceptions of and narratives about Patagonian nature that is already mediated by this dominant discourse. This dynamic suggests that VGI constitutes a new form of discursive power that digitally reproduces and mobilises a dominant discourse of nature, (re)producing what we term ‘discursive digital nature’.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".