A Framework for Integrating Information Ethics (IE) in the Curricula for Africa
Bibliographic record
Abstract
The debate about embedding information ethics (IE) in the curriculum in Africa is gaining momentum as scholars from developed and developing world engage on the subject. Some research publications are starting to emerge on information ethics in Africa but so far they have been confined to addressing the extent to which information ethics is necessary, who should offer information ethics and why, who should be taught and at what levels, the duration of offering the course/program and the content that should be included in the curriculum. Little attention has been placed on the theoretical framework that should underpin IE curriculum for Africa as well as the sources of IE content for the curriculum. This paper therefore addresses the following issues: rationale for integrating information ethics in the curricula in Africa; theoretical and institutional framework for IE curriculum; potential sources of content for information ethics curricula, challenges of integrating information ethics into the curricula in Africa and prospects for integrating IE into the curricula in Africa.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".