Bibliographic record
Abstract
This article problematizes the concept of data and experiments with rhizoanalysis to think data in terms of problems, questions, and concept creation. At issue is representation and interpretation, foundational blocks of qualitative research that are incommensurate with rhizoanalysis and post-qualitative research. Deleuze has problematized this issue through questions and experimentation within an asymmetrical world filled with paradoxes that gave rise to concept creation of sense and nonsense. In the Logic of Sense, sense is the event itself. Sense emerges out of nonsense. In this article, representation and interpretation are deterritorialized (virtual becoming) and reterritorialized (actualized) as nonsense–sense and palpation. Palpation is a concept that refers to data experienced indirectly. Rhizoanalysis is deployed because of its non-hierarchical and non-linear approach to data. Multiple Literacies Theory follows a similar path. It releases school-based literacy from its privileged rank to engage reading in multiplicitous and heterogeneous rhizomatic connections. Reading a data assemblage is untimely and not pre-given. It plugs into Multiple Literacies Theory, an analytic approach to reading an assemblage in rhizoanalysis. Reading a data assemblage is explored in a study on how writing systems in multilingual children function and what writing systems produce through affect. A rhizomatic approach is proposed and constituted through a research assemblage whose differential elements enter into a relationality of affect that flows through and transforms the assemblage. It produces a movement that dissolves dualisms in favor of multiplicity, uncertainty, and the untimely. It decenters the cogito human, maps assemblages, and extends experiences of a material world. Posing problems and questions open paths to a future yet to become.
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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 | 0.438 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.018 | 0.065 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".