Bibliographic record
Abstract
HarassMap is an Egyptian organisation that works to create an environment where sexual harassment is not tolerated, and where individuals and institutions take action against it. For the purpose of this project, the project team cleaned up, organised, and made openly available for the public to access and use through a web portal, three main types of data: Crowdsourced reports of sexual harassment incidents (reports on HarassMap’s online reporting and mapping system) - CSV and XLS Field data from HarassMap’s research on sexual harassment using traditional qualitative and quantitative research methods - DOCX, PDF, SAV, MP3 Social media conversations (comment threads and messages related to sexual harassment on harassMap’s Facebook page) - XLS The social media data was collected retrospectively from our Facebook page during the project period and covers the period 2010-2016. The crowdsourced data and the research data was cleaned and organised to make sure it is usable for the public but still kept in its raw format. During the collection and organisation period, we also made sure to clear out all personal identifiers from the data to ensure anonymity and confidentiality, and prepared descriptions of each dataset that will help the public understand how the data was collected and how it can and cannot be used. The data is stored online on a web portal that we built together with a web developer during the project period. On the web portal, the data is available for the public to view, search and download for research or other purposes. The data is also backed up on a hard drive and the cloud. The web portal and HarassMap open data will be advertised on our website, and the direct link shared with our contacts and others who approach us with interest in our data.
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 | 0.026 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.530 | 0.210 |
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".