Exploring the Meaning of Education and Teaching in an International Context
Bibliographic record
Abstract
In this presentation we will share insights gained from participating in an International Doctoral Research Seminar held in Beijing, China in 2018. As four graduate students accompanied by two faculty members, with diverse life and educational narratives, we bring our individual perspectives into dialogue as we make sense of our participation leading up to, during, and after the seminar experience. While at the seminar we engaged with colleagues from China and Australia around the theme: Teacher Education: Theories and Practices from an International Perspective . As teacher education practices become increasingly connected and transformed through processes of globalization, technology and demographics (Townsend, 2011), we are challenged by deep questions around education, knowing, teaching, and learning. Thus, through the use of narrative inquiry, we remain open to intersect our particular ways of doing education, while we engage with seminar participants, and overlap narratives to frame our understandings. This paper is centered on the question: What does it mean to educate, and to be educated, in a global context?
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".