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Record W2593664517 · doi:10.3138/cart.52.1.3574

The Importance of Context: Assessing the Benefits and Limitations of Participatory Mapping for Empowering Indigenous Communities in the Comarca Ngäbe-Buglé, Panama

2017· article· en· W2593664517 on OpenAlexaffvenue
Derek Smith, Alicia Ibáñez, Francisco Herrera

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsIndigenousCitizen journalismGeospatial analysisContext (archaeology)Participatory GISSocioeconomic statusEmpowermentPoliticsEnvironmental planningGeographyResource (disambiguation)Political scienceEnvironmental resource managementPublic relationsSociologyPopulationCartographyEcologyComputer science

Abstract

fetched live from OpenAlex

Indigenous communities have been involved in participatory mapping projects to protect their territories and manage their resources for decades. However, while tremendous advances have been achieved in many settings, the use of maps by indigenous peoples is very uneven. Here we present the case of a team of university researchers, indigenous students, and local investigators who used a participatory approach to map cultural landscapes and mature forest cover in the Comarca Ngäbe-Buglé of Panama. This article examines the success and limitations of efforts to empower indigenous people in the region to use mapping tools for conservation and resource management. The project, while it provides a useful example of how to build a participatory research team to produce maps that better reflect indigenous points of view, fell short of empowering indigenous authorities to use geographic tools to manage their territories. This is due mainly to the lack of administrative capacity needed to make use of geospatial information. We argue that cartographers involved in participatory projects, while typically attentive to the problems of marginalization, need to pay more attention to the broader socioeconomic contexts of their work and to redouble their efforts to respond to the challenges of the digital divide, which is a symptom of broader socioeconomic and political inequalities stemming from the legacies of colonialism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.383
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2017
Admission routes2
Has abstractyes

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