The Politics of Purple: Dyes from Shellfish and Lichens
Bibliographic record
Abstract
Dyes from shellfish ('murex') and lichens ('orchil') originated before 1000 BCE. Murex and orchil became symbolic of Roman privilege. Purple also attained iconic value as an emblem of wealth and power. Politics played a role in dye manufacture, as witness the male-dominated purple dye works which were later 'invaded' by female workers. This present study investigates these political issues by replicating the actual murex and orchil method. As we are a female team, our work here confronts references to so-called secret methods which women were (according to earlier historians) unlikely to grasp. Experiments undertaken by our Japanese- Canadian team have revealed features of puple which shed light on political issues. For example, murex and orchil were often used TOGETHER, a strategy that may have developed as a response to depletion of one organism or the other at times of ecological stress. Ancient texts hint at murex/orchil combinations as subterfuge, interpreted by some present historians as fraudulent dyes. By contrast, our work shows that together, murex and orchil produce an IMPROVED dye, one with enhanced fastness and great beauty. There was also a concomitant economic bonus with murex/orchil dyes. Murex was more labour-intensive, and so adding orchil saved time and money. Our replication of murex and orchil purples provides a lens through which to view political and cultural aspects of ancient purple manufacture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".