The gender fault line of Haiti s 2010 earthquake: The fight for women s bodies
Bibliographic record
Abstract
This thesis examines the subject of gender based violence (GBV) in Haiti and how local and international organizations are addressing the issue on multiple levels.Due to historical, political and economic factors, the earthquake that struck the country on January 12 th 2010 had devastating impacts.The earthquake worked as a catalyst that revealed deep underlying gender fault lines that had developed over long periods of time.To explore the construction of Haitian women's gendered vulnerability to disaster and how the issue of GBV is addressed, I analyze information gathered from interviews with eight different organizations working with anti-GBV projects Haiti.I argue that addressing the cultural attitudes underlying socially constructed gender inequalitiesand the way they are expressed and upheld by structural violenceis the most important element in designing efforts to reduce GBV.VI VII Map 1: Political and Administrative Map of Haiti.Nations Online Project.Map 2: "Top panel: focal mechanisms for 50 earthquakes of the 2010 January 12 Haiti main shock-aftershock sequence.Bottom panel: focal mechanisms for four earthquakes occurring in 1990-2008, prior to the 2010 main shock.All events are plotted at the NEIC epicentral locations.The mechanisms shown in grey are less well constrained than those shown in red" (Nettles & Hjörleifsdóttir, 2010, p. 376).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".