TOWARD LEARNING GRID INFRASTRUCTURES: AN OVERVIEW OF RESEARCH ON GRID LEARNING SERVICES
Bibliographic record
Abstract
The Learning Grid refers to the promise of projects that pool together instructional materials on distant computers. The Grid provides a wide range of available and potential learning services and resources and does not simply refer to taking advantage of the multiplying effects of connectivity. It supports the personalized use of the collective intelligence provided by networked computers and supports the exchange, negotiation, and dialogue within and among virtual, evolutionary, and pervasive learning communities. This article provides an overview of papers from the first workshop on Grid Learning Services, which brought together researchers discussing their views of infrastructure, services, and resources. It also addresses several research questions, including: What are the relevant resources and services and how can they be identified or built? How do they rely on the basic open Grid service architecture? How can intelligent tutoring systems be built on the Grid? How do the performance, efficiency, usability, and the global ability of those services meet individual and collective users' expectations?
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".