Bibliographic record
Abstract
Binaries affect many aspects of educational discourse including research and teaching. Although not every binary is negative towards educational 'forward' movement, the authors propose that rhizomatic thinking, derived from the writing of Deleuze and Guattari, can open new potentialities for a breaking of different types of binary thinking. Adopting the terminology of rhizomatic research they outline ways that re-envision educational research through the concept of the rhizome, as a hopeful pathway towards new ways of teaching and research. As a guiding quasi-methodology, rhizomatics could help researchers/teachers develop agency but step beyond personal agency to see research/teaching through multiplicities that arise rather than pre-planned forged curricula. Starting in the middle, the authors suggest that rhizome researchers recognize their embeddedness, allow research to lead them, accept that attempts to synthesize are never finished, listen to those before them and on the margins, and give themselves to a life of becoming, thus 'breaking' the binaries that can capture or stifle their attempts to be educational researchers constructing symbolic selves.
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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".