Bibliographic record
Abstract
HarassMap is an NGO based in Cairo that collects and maps crowdsourced data on sexual harassment in Egypt. Alongside this crowd-sourced data gathering it also offers training, workshops and advocacy programs, working with relevant parties to reduce the acceptability of all forms of sexual harassment. The project has been running since 2010 based on the Ushaidi platform. Over this time it has collected a very large number of mapped events reported largely by anonymous members of the public. The data has value both in terms of its richness; mapping data, category of harassment and descriptions are all recorded; and also as a longitudinal dataset that can inform on the success of interventions as well as the development of new forms of harassment. The project has been approached in the past by a number of researchers interested in using the data it has collected. The interest from HarassMap in Pilot Project participation was originally to obtain technical support to address how best to share data. While some technical advice was offered the focus on practice and planning was still useful. Identifying what data resources the project had, and in what form, allowed them to develop an online portal through which data can be made available to researchers on request.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".