Critical Change in the Educational Landscape: Reimagining, Reengineering, and Redesigning a Better Future
Bibliographic record
Abstract
Undoubtedly, the entire globe is in the middle of a transition process owing to the forced impact of the COVID-19 pandemic that has urged us to change radically and critically. Motivated by the need to understand ongoing changes, this editorial intends to surface crucial issues that can possibly impact and shape the educational landscape and its future. In this sense, this editorial sees the COVID-19 pandemic as a triggering event and explores issues that are significant for educational systems. In essence, the normal as we knew it was problematic and the crisis that emerged with the COVID-19 pandemic can be an opportunity to transform the educational systems that were accustomed to rigid structures.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".