Digital Cartography and Feminist Geocriticism: A Case Study of the Marichjhapi Massacre
Bibliographic record
Abstract
Dalit massacres in India are an understudied area of research, with even fewer works on the female experiences of the massacres. As part of a larger study that aims to create a spatial archive of the female survivors of selected Dalit massacres, this article maps the female survivors of the Marichjhapi massacre (1979). Being the first prototype of the forthcoming archive, a thorough analysis of the massacre is performed here using feminist geocriticism and digital cartography. The introduction gives the background to the massacre and foregrounds the absence of female narratives surrounding the massacre. The next section addresses the gaps in understanding the relation between space, caste, and gender in Dalit scholarship. The methodology section explains the steps involved in a feminist geocritical and digital cartographical approach, which is a combination of both qualitative and quantitative research. The prototype of the cartographic visualizations using QGIS software constitutes the next section, along with a visualization of the results and analysis of the data. Dalit female experiences are foregrounded through a close reading of selected texts, both fictional and non-fictional. This will eventually result in the creation of an archive of female historiography by locating the survivors at the site of the massacre.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| 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".