[Telescience : Feasibility studies, definition and a fair answer to the scientific brain drain].
Bibliographic record
Abstract
BACKGROUND: What is telescience? Is it feasible to transfer academic information with the help of telematics to educate and teach young scientists over large distances? The term telescience has so far not been defined but covers a variety of possibilities, which could be successfully implemented worldwide. This article gives examples and highlights the feasibility analysis of telescience. METHODS: We have carried out feasibility analyses for neurological functional diagnostics, an epidemiological cross-sectional study as well as a laboratory study for detection of thrombocyte function during dengue fever with the help of telemedicine. The basis for all these projects was a telemedical transcontinental cooperation over a distance of 12,000 km. RESULTS: All performed studies demonstrated the feasibility. With the help of telematics the laboratory techniques, planning, conduction and interpretation of results as well as publication skills can be transferred. DISCUSSION: Telescience is feasible. Our studies showed that telescience is a very promising option to transfer knowledge, which will help to enable professional expertise to be transferred directly to the region/country without a brain drain. All too often young motivated scientists are enticed to move to well-known institutions, which involves the danger of a brain drain. Brain drain can be avoided in favor of local implementation of scientific projects. Our results illustrate that it is feasible to educate and guide scientists with the help of telematics infrastructures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".