Ethical and Moral Evaluation of African Digital Humanities: Methodology and Preliminary Results
Bibliographic record
Abstract
This poster presents the preliminary findings of ongoing doctoral research examining the information behavior of scholars of African digital humanities (ADH). The study focuses on ethical and moral considerations in developing digital visualization tools such as GIS maps and 3D visuals showcasing information on sensitive topics such as slavery. This research employs a qualitative analysis of data collected through in-depth, semi-structured interviews aiming to 1) identify the challenges faced by scholars of African origin/descent in developing or using such visualization tools and 2) record their recommendations to address such challenges. Analyses of the first five interviews reveal prominent challenges faced by participants, including lack of technical skills, usability issues, content-related complexity, and the representation of primary source material. To overcome these challenges, the participants recommend collaboration, inclusive research designs, and involvement of community members. The five pilot interviews have allowed adjustments to the interview guide for an additional 10--13 prospective participants. This work aims to produce a guide for digital humanities scholars and professional web developers on designing efficient websites that showcase the results of historical research in an ethical and morally considerate manner.
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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 | 0.087 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".