Bibliographic record
Abstract
During the ten years that were spent in editing the three African Series volumes, the Marcus Garvey and Universal Negro Improvement Association Papers project has incurred an unusually large number of institutional, intellect jal, and personal debts.The preparation of the volumes would never have bee i possible without the continuing support and assistance of a wide array of nanuscript librarians, archivists, university libraries, scholars, funding age icies, university administrators, fellow editors, and friends.While the debts thus accrued over the past decade can never be adequately discharged, it is still a great pleasure to acknowledge them.They form an integral part of whatever permanent value these volumes possess.We would like here to acknowledge our deep appreciation to so many for contributing so greatly to this endeavor.In a real sense, these volumes represent the fruition of the efforts of many hands that have worked selflessly to assist in documenting the story of the African Garvey movement.We would like to thank the many archives and manuscript collections that hav ; contributed documents as well as assisted the project by responding with unfiiling courtesy and promptness to our innumerable queries for informador : American Colonization
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.238 | 0.139 |
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".