Towards Professionalism and Commitment in Africa: the case for theory and practice of Information Ethics in Uganda
Bibliographic record
Abstract
Though one could not exhaustively and conclusively define all the attributes of an information/knowledge society, it seems Uganda has made commandable strides to such society. One of the prerequisites of such society is a corps of well-educated, trained and experienced information professionals to manage information and knowledge effectively in that society. Furthermore, the corps must perform professionally and ethically at all times. To fulfill this, an Information Code of Ethics (ICE) is required. However, Uganda has as yet to establish an ICE because of some reasons. These reasons are outlined in the paper. The ICE has two as-pects: theory and practice. These concepts are explained and related. The LA (USA) and the Chartered Institute of Library and Information Professions (CILIP) formerly the LA (UK) are studied as living examples. The provisions of these ICEs are briefly explained. In view of the past experience a process of establishing an IME for Uganda is considered. The paper concludes with an outline of the content of an IME for Uganda.
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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.068 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".