Bibliographic record
Abstract
The pandemic has been a watershed in history, and we are embarking on a new era. We need to seize this opportunity to reimagine pedagogy in more ethical terms. This chapter asks: What does it mean to have equality in a world that is confronted with diversity and difference in multiple ways? Is education a human right? What is education's role in removing inequality at a time when the gap in inequality is growing among nations and within societies? If we agree that “universal and lasting peace can be established only if it is based upon social justice” (preamble to the i lo Constitution, 1919, p. 1), as educators we need to seize this historical turning point to make education the “great equalizer”. The focus then is on a worldview that is inclusive of all children in the learning process, irrespective of location and their differences. This change in perspective involves a paradigm shift, and underscores the importance of teacher education. In an interdependent world, both peace and our survival are contingent on an equitable and cohesive planet. Education has the potential and the responsibility to prepare the future generation for responsible behavior towards a more equitable and socially cohesive world that preserves the environment and maintains peace.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".