Libraries and Information Services: Towards the Attainment of the UN Millennium Development Goals
Bibliographic record
Abstract
Libraries and Information Services: towards the attainment of the UN Millennium Development Goals; edited by Benson Njobvu and Sjoerd Koopman (IFLA PubUcations 134). Munchen: K.G. Saur, 2008. 224 pp. ISBN 9783598220401. EUR 79,95 (EUR 73,50 for IFLA Members), Price for USA, Canada, Mexico: US$ 112.00 (US$ 84.00 for IFLA Members) This compUation of thirteen papers, chosen from those presented at the eighteenth SCECSAL (Standing Conference of East, Central and Southern Africa Library and Information Associations) Conference in Zambia in July of 2008, examines the MUlennium Development Goals (MDGs) that have been targeted by 192 United Nations member states for achievement by 2015. The papers attempt to shed light on how Ubraries and information services can aid in the attainment of these goals. Although it is not clear from the title of the volume, aU of these papers focus on African Ubraries and information providers and most papers suggest solutions for the SCECSAL region of Africa. Since the United Nations endorsed the Millennium Declaration in 2000, Ubraries and information professionals have been trying to determine their place in contributing towards the achievement of the following eight MUlennium Development Goals: 1. Eradicate extreme poverty and hunger. 2. Achieve universal primary education. 3. Promote gender equality and empower women. 4. Reduce child mortality. 5. Improve maternal health. 6. Combat HTV/ Aids, Malaria and other diseases. 7. Ensure environmental stability. 8. Develop a global partnership for development. The challenges and strategies required for attaining these eight goals along with their twenty-one targets are addressed by African LIS professionals in the articles in this volume. It seems clear that libraries and information centres should be involved in providing relevant information to assist in the kind of development efforts required to attain these goals. Why is it that they have not been more successful? The answers to this question along with specific suggestions and solutions for future implementation could help renew efforts towards attaining these goals. The keynote paper by Kaniki presents a good overview of the intention of the conference and its papers. He outlines opportunities for African libraries and information services to contribute to the MDGs and he challenges them to do what they can to help achieve these goals. He suggests that all libraries should begin by providing open access to information about the MDG goals, therefore enabling their users to be aware of and act towards achievement of these goals. Peter Lor's article is a valuable reference work which focuses on the multitude of programs and the alphabet soup of acronyms which label many programs which are essential in the growth of information distribution for the good of society. He sheds light on the challenges and opportunities for LIS professionals to contribute through the various programs of WSIS, UNESCO, IFAP and others. Justin Chisenga's article on e-agricultural initiatives presents four innovative ICT applications, which offer pragmatic solutions for the attainment of the first MDG, the eradication of extreme poverty and hunger. This well-written article emphasises the necessity of sharing experiences which may provide workable solutions to further agricultural successes on a global scale. …
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.022 |
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".