Literature on Humanity Campaign: Facts from Quaker Writers in the United States
Bibliographic record
Abstract
This article discusses some facts about Quaker writers who used literature as a tool to advocate for the human values of minorities in the United States. Along with the objective, this article puts forward some Quaker writers in the United States, especially in the pre-twentieth era. Those writers are John Woolman, John Whittier, Elizabeth Chandler, Angelina Grimke, and Sarah Grimke. This is a qualitative writing using an interdisciplinary approach through which it presents literary works and the contexts experienced by Quaker writers. It is found that those writers consistently published essays, pamphlets, letters, and poetry. Along with their writings, the Quakers promoted humane values as expressions of their opposition to social injustice. In this regard, there are two main issues the Quakers consistently dealt with, i.e., the abolition of slavery faced by African Americans and the need for emancipation for women from the patriarchal system. This consistent attempt was made because of the beliefs of Quakerism, which acknowledge the presence of the Inner Light (Jesus Christ) in all human beings regardless of racial and gender differences. As a consequence, this fact serves as proof from the Quakers for all people that religious belief can be a trigger to persistently campaign for humanity's values and goodness for minorities. Besides, this facts proves that literature can be a tool in campaigning human values and fighting against inhumanity.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".