Visualization Tools for African Digital Humanities: Scholars’ Perspectives on Ethics and Morality
Bibliographic record
Abstract
My doctoral research examines the information behavior of African scholars. I am reviewing their ethical and moral perspectives towards developing and using digital visualization tools such as GIS maps and 3D models on research websites, including digital archival collections of historical artifacts on the history of African slavery. These collections are housed and showcased through digital visualizations at various archives, courthouses, museums, and libraries. My research employs a qualitative study using in-depth, semi-structured interviews to 1) understand the meaning of ethical and moral research in digital spaces for African history, 2) investigate how and why African scholar uses visualization tools, 3) identify the challenges faced in developing or using such tools, and 4) record recommendations to overcome such challenges. I am inductively coding verbatim transcripts of the interviews to develop themes that address my research questions. My study design aims to reflect on the perspectives of marginalized communities with a sensitive background history of trauma that leads to the horror of racism and discrimination to date. Thus, this work applies more widely to digital libraries and archival studies that intend to develop digital tools sensitive to the ethical and moral implications of the information they contain.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".