A Discussion of the Tubman Arias from the Opera Harriet Tubman: When I Crossed That Line to Freedom
Bibliographic record
Abstract
Nkeiru Okoye (1972-) is an American composer of operas and orchestral works whose concert and theatrical compositions have gained extensive recognition in recent years. Harriet Tubman: When I Crossed that Line to Freedom is Okoye’s most well- known opera for which she composed the music and penned the libretto. Through her compositions, Okoye bravely addresses political and racial issues and tells the stories of well-known Black figures, such as Harriet Tubman. Harriet Tubman (1822-1913) was a prolific American hero that led runaway slaves toward freedom. For approximately a decade, she risked her life traveling to and from the South to family and friends, helping them safely reach liberation in the North. Throughout her lifetime, Tubman advocated for abolitionism, women’s suffrage, and served in the Civil War. She lived a selfless life that was fully dedicated to helping others. In 2014, Nkeiru Okoye’s opera premiered, telling the story of Tubman’s life and various journeys. This opera has received critical acclaim and is frequently performed throughout the United States and Canada. In the opera, Tubman’s character sings four arias, each representing a different time in her life. The primary purpose of this essay is to present this work to the academic literature by examining the four arias composed for Tubman’s character. I aim to bring awareness to the composer, opera, and prolific historical figure who inspired this piece. A secondary purpose is to provide a resource for iv those performing the Tubman arias or any selections from the opera. This document provides detailed insight that will uplift future performances of the opera Harriet Tubman: When I Crossed that Line to Freedom.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".