Bibliographic record
Abstract
This article interrogates principles of ethnography in education proposed by Mills and Morton: raw tellings, analytic pattern, vignette and empathy. This article adopts a position that is uncomfortable, unconventional and interesting. It involves a deterritorialization/ rupture of ethnography in education in order to reterritorialize a different concept: rhizoanalysis, a way to position theory and data that is multi-layered, complex and messy. Rhizoanalysis, the main focus of this article is not a method. It is an approach to research conditioned by a reality in which Deleuze and Guattari disrupt representation, interpretation and subjectivity. In this article, Multiple Literacies Theory, a theoretical and practical framework, becomes a lens to examine a rhizomatic study of a Korean family recently arrived to Australia and attending English as a second language classes. Observations and interviews recorded the daily lives of the family. The vignettes were selected by reading data intensively and immanently through a process of palpation, an innovative approach to educational research. Rhizoanalysis proposes to abandon the given and invent different ways of thinking about and doing research and what might happen when reading data differently, intensively and immanently, through Multiple Literacies Theory. Rhizoanalysis, a game-changer in the way research can be conducted, affords a different lens to tackle issues in education through research.
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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".