Grey literature and the DEVSIS-Botswana Project: The Case of the national institute of development research and documentation
Bibliographic record
Abstract
The NIR Documentation Centre serves the research staff at NIR and other members of the public. It collects, organises and disseminates grey or unpublished literature. In 1984 a project was formulated between the Documentation Centre and the International Development Research Centre, Canada and the Pan African Documentation and Information System (PADIS). The Project sought to assist the NIR to effectively collect and organise its holdings using PADIS methodologies. It would also contribute to the bibliographic database at PADIS and would eventually computerise its own holdings. This was part of a grand scheme to establish a network with PADIS as the hub and with various regional nodes contributing and using the databases at PADIS. Centres like the NIR would be national centres through which other centres in the country would contribute and access the PADIS database. However, NIR itself would gain access to PADIS through the regional mode, to be known as SADIS. This paper will outline the experiences of NIR with such a project and what the results or outcome of this project were. In brief, NIR participated in this project, using PADIS methodologies of processing documents and computerising its holdings. The NIR developed to such an extent that they were ultimately able to develop their own database and to produce DEVINDEX-Botswana without any assistance. The envisaged network however never really took off. The paper will also look at some of the problems that impeded the DEVSIS project and what the project meant for the achievement of effective organisation and dissemination of grey literature.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".