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
Abstract The term ‘lifelong’, as applied to education or learning, has been in circulation for more than a quarter of a century. It has played an important role in policy discussions, as well as in studies of the sociology and economics of education. The relationship of this term to the rapidly changing world of information and educational technologies, and to the various conceptions of interaction that are central to these technologies, however, has been considered much less frequently. This paper seeks to shed light on the relationship between lifelong learning and the interactive technologies that have become associated specifically with the Semantic Web. It begins by presenting a fictional narrative to illustrate a lifelong learning scenario in the context of the services and resources that the Semantic Web will be capable of providing. It then proceeds to isolate a number of general characteristics of lifelong learning as they are manifest in this scenario and in recent literature on the subject. The paper then explores how emergent, interactive technologies of the Semantic Web have the general potential to address many of the characteristics of lifelong learning, and hold out the promise of satisfying a wide variety of lifelong learning needs. It will conclude by considering some of the outstanding challenges presented by lifelong learning contexts, and mention some of the limitations of advanced technologies used to address these needs.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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