A Framework to Leverage and Mature Learning Ecosystems
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
With the average shelf life of an employee’s skills at less than five years, it is im-perative that organizations support their employees in staying current in new and emerging skills and in learning how to learn. Learning management systems, once seen as a one-size-fits all learning solution, have not effectively kept pace with wider technology development, and the needs and expectations of workplace learning. Moreover, organizations tend to have too narrow a view when consider-ing the elements that affect learning at their organization. An ecological and holis-tic approach is needed to improve learning environments and to future-proof these environments for new developments in education and technology. This pa-per explores the existing literature and frameworks for learning ecosystems and proposes a new learning ecosystem framework that consists of seven key ele-ments: (1) technology and data architecture, (2) governance, (3) analytics, (4) se-mantic ePortfolios, (5) intrinsic and extrinsic motivators, (6) social learning and engagement, and (7) personalization.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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