Twenty-First Century Learning as a Radical Re-Thinking of Education in the Service of Life
Bibliographic record
Abstract
The education reforms called for in 21st century education initiatives have been characterized as radical. International efforts to reformulate education for 21st century teaching and learning are well-funded initiatives by coalitions including governments, not-for-profit organizations, and large corporations. This article is a critique of the emergence of 21st century learning showing that a preoccupation with competencies and skills can be interrogated for that to which 21st century learning gives voice, but also for that which it silences. The fundamental question of the purpose of education, or for what do we educate, is virtually absent in most discussions of 21st century learning. Finally, I offer an alternative curricular vision to the techno-optimistic belief in progress prevalent in the discourse of 21st century learning. In the call for radical reform, I propose another understanding of the word “radical,” one that includes an ecocentric, life affirming understanding that roots education in a life code of value and in a living community of relations large enough to embrace the multidimensionality, the responsiveness, and responsibility at the heart of the pedagogical relation.
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.116 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".