Human Geography, Indigenous Mapping, and the US Military: A Response to Kelly and Others’ “From Cognitive Maps to Transparent Static Web Maps”
Bibliographic record
Abstract
In 2017, Cartographica published an article that criticized some human geographers for misguided oversensitivity to the use of funding from the US military to map indigenous lands. According to Kelly and others, geographers who map indigenous lands with funding from the US military – as they have done in Honduras – do not compromise the discipline’s ethical norms as long as they openly reveal their source of funds. We re-evaluate this claim by considering the specific source of funding used by Kelly and others to map indigenous lands in Honduras: the US military’s Minerva Research Initiative. Awards from Minerva, we show, are neither arbitrary nor based principally upon scholarly evaluation. Rather, the program is organized to increase the power of the US military through the development of new tactics and weapons through collaboration with social scientists. We conclude by discussing implications of our critique of Kelly and others for the ongoing debate regarding the involvement of the US military in the discipline of geography.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".