Bibliographic record
Abstract
Exploring the intersections of digital humanities and African diaspora studies How can scholars use digital tools to better understand the African diaspora across time, space, and disciplines? And how can African diaspora studies inform the practices of digital humanities? These questions are at the heart of this timely collection of essays about the relationship between digital humanities and Black Atlantic studies, offering critical insights into race, migration, media, and scholarly knowledge production.The Digital Black Atlantic spans the African diaspora’s range—from Africa to North America, Europe, and the Caribbean—while its essayists span academic fields—from history and literary studies to musicology, game studies, and library and information studies. This transnational and interdisciplinary breadth is complemented by essays that focus on specific sites and digital humanities projects throughout the Black Atlantic. Covering key debates, The Digital Black Atlantic asks theoretical and practical questions about the ways that researchers and teachers of the African diaspora negotiate digital methods to explore a broad range of cultural forms including social media, open access libraries, digital music production, and video games. The volume further highlights contributions of African diaspora studies to digital humanities, such as politics and representation, power and authorship, the ephemerality of memory, and the vestiges of colonialist ideologies. Grounded in contemporary theory and praxis, The Digital Black Atlantic puts the digital humanities into conversation with African diaspora studies in crucial ways that advance both. Contributors: Alexandrina Agloro, Arizona State U; Abdul Alkalimat; Suzan Alteri, U of Florida; Paul Barrett, U of Guelph; Sayan Bhattacharyya, Singapore U of Technology and Design; Agata Błoch, Institute of History of Polish Academy of Sciences; Michał Bojanowski, Kozminski U; Sonya Donaldson, New Jersey City U; Anne Donlon; Laurent Dubois, Duke U; Amy E. Earhart, Texas A&M U; Schuyler Esprit, U of the West Indies; Demival Vasques Filho, U of Auckland, New Zealand; David Kirkland Garner; Alex Gil, Columbia U; Kaiama L. Glover, Barnard College, Columbia U; D. Fox Harrell, MIT; Hélène Huet, U of Florida; Mary Caton Lingold, Virginia Commonwealth U; Angel David Nieves, San Diego State U; Danielle Olson, MIT; Tunde Opeibi (Ope-Davies), U of Lagos, Nigeria; Jamila Moore Pewu, California State U, Fullerton; Anne Rice, Lehman College, CUNY; Sercan Şengün, Northeastern U; Janneken Smucker, West Chester U; Laurie N.Taylor, U of Florida; Toniesha L. Taylor, Texas Southern U.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".