The cartographic ambiguities of HarassMap: Crowdmapping security and sexual violence in Egypt
Bibliographic record
Abstract
Abstract In December 2010, HarassMap was launched as a Cairo-based interactive online mapping interface for reporting and mapping incidents of sexual harassment anonymously and in real time, in Egypt. The project’s use of spatial information technologies for crowdmapping sexual harassment raises important questions about the use of crowdsourced mapping as a technique of global human security governance, as well as the techno-politics of interpreting and representing spaces of gendered security and insecurity in Egypt’s urban streetscape. By recoding Egypt’s urban landscape into spaces subordinated to the visual cartography of the project’s crowdsourced data, HarassMap obscures the complex assemblage that it draws together as the differentially open space of the Egyptian street – spaces that are territorialized and deterritorialized for authoritarian control, state violence, revolt, rape, new solidarities, gender reversals, sectarian tensions, and class-based mobilization. What is at stake in my analysis is the plasticity of victimage: to what extent can attempts to ‘empower’ women be pursued at the microlevel without amplifying the similarly imperial techniques of objectifying them as resources used to justify other forms of state violence? The question requires taking seriously the practices of mapping and targeting as an interface for securing public space.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".