Distance Education But Beyond: “meLearning”—What If the Impossible Isn't?
Bibliographic record
Abstract
It's not the physical distance between teachers and learners that is the problem but the gap between what all of us as learners need and what those who supply the education, training, and learning have to offer. Mass-produced education for the masses will give way to massively customized learning with contributions from all. While the scope of such an endeavor appears staggering, the dream of adaptive, personalized "meLearning" for every person every day, and thus the brightest future ever imagined, lies immediately in front of us and within our grasp. The vision for a future world state where every person experiences personalized learning every day is clear, but it has arrived prematurely, is inequitably distributed, and remains unseen by most. Moreover, and as has been common in the history of conditions preceding such mass revolutions, the existing organizations and institution stand in stark contrast to this vision and, in many ways, oppose it. All quite predictable and understandable. However, the status quo is no longer an option. The distance between the supply side of the equation (solutions offered) and the demand side (demands and needs) is disproportionately large. Change is inevitable, and is increasingly demanded by all involved. If you have not done so already, it is time to start imagining what our world would be like if we increased the effectiveness of learning for everyone, all the time, by customizing the learning to fit each individual person and situation. Imagine if the impossible isn't! Many others already have. This future is ours for the choosing if we can muster the courage to ignite the transformation from vision to reality by simply imagining that this bright future is now possible and beginning to shape its design and implementation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".