Project MapLemon: Peeling Back the Secrets of Queer Writing Through Stylometric Demographic Identification
Bibliographic record
Abstract
Project MapLemon is a corpus for stylometric demographic identification of 54,000+ words across 345 participants, originally created to obtain a baseline corpus for linguistic variation among North American English speakers. The corpus contains responses from 30 linguistic backgrounds, and 40 US states and 6+ Canadian provinces. Project MapLemon has innovated a new method for data collection for linguistic variants in the natural, digital written word. Project MapLemon utilizes a hand-drawn map and asks the participant to give directions via this map, as well as asking participants for a recipe for lemonade. In addition to its novel collection methods, MapLemon contains responses from 212 transgender and non-binary people; analysis of which has shown that transgender people write most similarly (based on parts of speech) to their sex assigned at birth, then to their gender, and are dissimilar in their writing to other opposite-sex transgender people. Furthermore, the analysis suggests that non-binary people are their own gender category and cannot be classed with any other gender.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".