User education for the Caribbean Information System
Bibliographic record
Abstract
User Education for the Caribbean Information System -Some Appropriate Methods and Techniques The concept of user education and training defined in an earlier session, is the one within which this presentation is framed.The paper considers some programme alternatives, methods and techniques which are likely to create among existing and potential users of CARISPLAN a greater awareness of the value of information for socio-economic planning and development and instill in these users positive attitudes leading towards the need to seek and use information.Suitable programmes which could provide some of these target users with skills to discover and search relevant information sources are outlined.Assumptions The paper assumes that the environmental situation has been carefully studied, goals and objectives have been formulated, target audiences and their information needs identified, and that existing and potential information and other suppõrt resources which may be necessary for programme implementation have been identified and ensured.Some programme alternatives may also have been examined and selected.Annex I of this paper is a modified outline of a plan of action which is distributed by LOEX Clearing House U, It indicates steps to be considered in the planning and implementation stages and may be consulted when planning yòur exercise for presentation on Friday.Promoting the Value of Information One of the objectives of CARISPLAN is to assist member states of the Caribbean Development and Co-operation Committee in developing an information
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.321 | 0.103 |
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".