The Virtual Learning Resource Center for the Digital Manpower
Bibliographic record
Abstract
This research aims to develop the models of Virtual Learning Resource Center (VLRC) for the digital manpower. The results of the research are that: 1) The VLRC development process consists of 5 steps: requirements analysis, planning and designing, prototyping and testing, implementing and monitoring, and evaluation and reporting. 2) The VLRC for the digital manpower consists of two models: 2.1) the physical learning resource, which is a physical space that the learner can actually touch. It can be divided into 5 categories: location-based learning resources, human-based learning resources, material-based learning resources, equipment-based learning resources, and event-based learning resources; and 2.2) the digital learning resource, which is a virtual space that learners can access through information and communication technology tools. It can be divided into 5 categories: search engine and translator’s tools, management and storage tools, distance learning tools, content creation, presentation and dissemination tools, social networking and online learning communities’ tools. 3) The goals of VLRC development are learning to know, learning to do, learning to live together, and learning to be.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".