Bibliographic record
Abstract
In this conversation rhizoanalysis is introduced as a way of processing through an assemblage involving research methodology, generation of data and analytical possibilities entwined within. As a research methodology, rhizomethodology (Author, 2009) is a way of putting the Deleuzo-Guattarian philosophical imaginary of rhizome (Deleuze & Guattari, 1987) to work; it is a way of working (with) data, complexly. With/in/alongside this methodological approach, the rhizoanalysis becomes the inquiry of the research, happening throughout the whole research process. The analysis is not a constant thing relegated to a place of its own, rather, the rhizoanalysis as ‘some of rhizome’ (Deleuze & Guattari, 1987, p. 9) happens throughout. With/in/through processes of thinking rhizome in flux, working rhizome (im)provis(at)ionally is an ongoing experiment with and exploration of my own thinking. Rhizoanalysis (dis)continuously (e)merges with/in/through every dimension of the thinking, ebbing and flowing with/in/through matters of always already becoming so that writing (about) rhizoanalysis is also affected by writing (the) methodology and doing (the) rhizoanalysis; nothing was/is separate or linear in the thinking or writing up~down of the research project drawn on here. Rather, there was/is an ongoing intermingling of data, methodology and analysis with theorising the literature and practicing the theory, each becoming the other(s).
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".