Bibliographic record
Abstract
The growing datafication of the world continues to be a pressing concern for critical geographers. Indigenous scholars are also challenging western research paradigms for under-representing the social effects that datafication imposes on Indigenous communities. This paper adds to these conversations by closely examining the problematic of carbon datafication in Indigenous places using the author's positionality as an Indigenous-Naga geographer. The author simulated carbon maps of Nagaland (northeastern India) to demonstrate the datafication of Indigenous places into carbon commodities, and then used the maps and his emic perspectives to interview Naga tribesmen and tribeswomen about carbon datafication. Selected interviews are highlighted in this paper to contextualize the social effects of carbon datafication on Naga epistemologies of forests, material reorganization of space, and carbon enclosures for global marketization. The paper also examines the limitations of alternative non-digital mapping, as well as the opportunities for locally repurposing GIS applications to involve and benefit Indigenous communities. Elements of local agency and the speculative effects of carbon markets are also discussed in the inter-tribal sociopolitical context of Nagaland.
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 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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.101 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".