Bibliographic record
Abstract
Twenty-first century processes, such as globalization and digitization, pose various challenges for primary, secondary, and post-secondary teacher education at both the formal and informal education levels. These challenges are addressed by innovators in the field of teacher education, i.e. teacher educators, pre-service teachers, in-service teachers, scholars and policy-makers. This edited volume explores future trends in three different spheres of teacher education: 1) pedagogies (emotive, reflective, cognitive, and didactic practices), 2) technologies (digital competencies, artificial intelligence in teaching, and the transformative potential of digital tools in intercultural learning), and 3) societies (multilingualism, attitudes towards literacies, societal polarization, and teacher shortages). The suggested innovations aim to bridge the gap between theory and practice by drawing upon the critical evaluation of theoretical approaches as well as the discussion of best practice examples. The chapters are situated in various countries, such as Vietnam, Canada, Argentina, Spain, Germany, the USA, Switzerland, Sweden, Italy, and, as a transnational cooperation, Palestine and the UK. The Future of Teacher Education: Innovations across Pedagogies, Technologies and Societies considers various models of teacher education (e.g. reflective model, competency-based model, etc.) and applies a multitude of different research methods (e.g. didactic analysis of teaching material, thematic analysis of reflections, etc.).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".