Zesting Up Stylometry with MapLemon: A Corpus for Stylometric Demographic Identification
Bibliographic record
Abstract
MapLemon is a corpus in its second iteration that was created to obtain a baseline corpus for linguistic variation among English-speaking North Americans. The MapLemon corpus currently houses upwards of 21,000 words across 185 participants, 10+ linguistic backgrounds, and 40+ US states and Canadian provinces. MapLemon also houses writing from 91 transgender and non-binary individuals. MapLemon presents a unique method for data collection in the virtual written medium and a corpus that has proven useful for identifying demographic information via writing style, otherwise known as stylometry.MapLemon est un corpus en sa deuxième itération qui a été créé pour obtenir un corpus de référence des variations linguistiques parmi les anglophones d'Amérique du Nord. Le corpus MapLemon contient actuellement plus de 21 000 mots provenant de 185 participants de plus de 10 origines linguistiques et de plus de 40 États américains et provinces canadiennes. MapLemon contient également les écrits de 91 personnes transgenres et non binaires. MapLemon présente une méthode unique de collecte de données dans le domaine de l'écriture virtuelle et un corpus qui s'est avéré utile pour identifier des informations démographiques par le biais du style d'écriture, également connu sous le nom de stylométrie.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".