Painted People: Humanity in 21 Tattoos
Bibliographic record
Abstract
In 1881, a writer in the Saturday Review called tattooing ‘an art without a history’. ‘No-one’, it went on, ‘has made it the business of his life to study the development of tattooing.’\nUntil now.\n\nPainted People is a beguiling and intimate look at an untold history of humanity.\n\nThe earliest tattoos yet identified belonged to Ötzi, the ‘iceman’, whose mummy allows us a brief glimpse into the prehistory of the practice. We know that over the more than five thousand years since he was tattooed, countless cultures have performed this ancient practice, and people in every corner of the world have been tattooed. For the most part, these fascinating histories remain stubbornly untold, and the secrets of Siberian princesses, Chinese generals and Victorian socialites have been hidden on the skin, under layers of clothing and under layers of history. Now with access to a wealth of new and unreported material, this book will roll up its sleeves and reveal the artwork hidden beneath them.\n\nIn Painted People, Dr Matt Lodder, one of the world’s foremost experts on tattooing, tells the stories of people like Arnaq, who was tattooed in keeping with her cultural and religious traditions in sixteenth-century Canada, and Horace Ridler, who was tattooed as a means to make money in 1930s London. And in between these two extremes, he describes tattoos inked for love, for loyalty, for sedition and espionage and for self-expression, as well as tattoos inflicted on the unwilling, to ostracise. Taken together, these twenty-one tattoos paint a portrait of humanity as both artist and canvas.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.038 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".