The Virtual Learning Resource Center for the Digital Manpower
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it