Comic-Strip Mining: Neo-Extractivism and Land Conflicts in Joe Sacco’s Paying the Land (2020) and Nelly Luna and Jesús Cossio’s La guerra por el agua (2016)
Bibliographic record
Abstract
From the oil pipelines that scar the Northwest Territories transforming the lives of First Nation communities to the forest fires that rampage across the Gran Chaco due to soy deforestation, the devastating impact of neoextractivism is writ large in the Americas. To the backdrop of ecological devastation and the struggle for natural resources, local peoples across the region are faced with the impact of cultural upheaval and displacement brought about by faceless multinational corporations. In this article, I address the way two comics artists have used the graphic form to address neoextractivism, highlighting how investigative comics can create counterimaginaries of exploitation by relating image-stories told by local inhabitants. In 2016, working in collaboration with Nelly Luna Amancio and Ojo Público, the Peruvian Jesús Cossio published the webcomic La guerra por el agua, a study of the impact of the Tía María mine in Ayacucho, southern Peru, an operation owned by the Mexican company Southern Copper, later also released as a newspaper-sized pamphlet version in 2018. And in 2020 the US-Maltese Joe Sacco – who spoke at the launch of La guerra por el agua – released his graphic work Paying the Land, an exploration of the upheaval caused to the Dene in the Mackenzie River Valley by both the Canadian state and mining enterprises. Though both journalistic works are cut through by social protest and political wranglings, they simultaneously demonstrate the power of the micropolitical – in which we might include the comic form itself – to transgress the narratives and imaginaries of big capital.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".