Bibliographic record
Abstract
In this paper, I reflect upon my journey of learning to do comparative education research over the last decade and half. It involves transnational moves from Japan, Canada, US, Australia and back to Japan where I encountered numerous ‘others’. I use my story of a series of relocations as an entry point for theorising what I mean by ‘negative’ comparative education. Drawing on Andrea English and the Kyoto School of Philosophy, I use the term ‘negativity’ in the philosophical sense. That is, it refers to affective experiences of discomfort, perplexity and confusion as an important catalysis for generative learning and unlearning. It is a story of learning to let go of the familiar language and frame of seeing the world and embracing disruption as a critical moment for new learning. In conclusion, I argue that negative comparative education offers a methodological stance that enables us to undertake comparative research in a manner that challenges the Eurocentric geopolitics of knowledge and hence to contribute to the pluriverse world.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".