Bibliographic record
Abstract
In this paper I share my personal journey into rhizomatic thinking. Here I illustrate how a rhizome opened new possibilites to my previously confusing learning process. As a vehicle I ask the question, when considering the pedagogical nature of place, how does the new facilitate currere? I am also taking the opportunity to write in a way that is new and unfamiliar to me because the conventional and acceptable have been unable to help me understand the meanings I am seeking. I felt uninspired among traditional styles of academic writing until I encountered the doctoral thesis of Warren Sellers where another way of seeing and writing is explored. This generative experience gave me the momentum to link past learnings in new rhizomatic ways and begin a discussion within this journal about how place and pedagogy connect. My visit to Cheonggyecheon Stream in Seoul provided me the place and my reading of the texts of Warren Sellers, Noel Gough, Chaim Soutine, Margaret Sommerville and Lloyd Rees gave me examples of others who have searched. As I remember my physical experience of this new place, the stream becomes the search, the bridges spanning it, the new understandings and scattered along the banks, the rhizomes grow. A new place facilitates currere. This journal provides a forum where possibilities are viewed as exciting (Doll, 2009, p. 71). Tentative steps into new spaces are welcomed. Above all, conversation oils the machine, here I can share my thoughts with others who are exploring learning in diverse ways and from non linear perspectives.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 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".