Bibliographic record
Abstract
With the intention of expanding educational conversations through playful encounters, we braid curricular intensities inspired by wild profusions, written in our academic hair and offered as expressions of life writing. Through our hairatives, we share discomforts and provocations that are the stories of our scholarly identities, rooted in the body-word nexus as affective attunements. Through our entanglements, we map our networks of relations and invite curricular conductivity concerning how and why hair is formative in the context of the academy. Living on the precarious margins of stories, we share our narratives within the folds of educational theory to passionately and poetically render our richly textured events as the moments of knowledge creation. In this way, our hair serves as an artistic configuration, where we are manifest in “situated inquiry about the truth that it locally actualises”, to borrow from Badiou (2005), opening what may be described as an “eventual rupture” of our scholarly truths (p. 12). Our ruminations are the imaginaries of academics, or simply living intensities. We intend to crack open from the inside that which is “a reality concealed behind appearances” in an attempt to reconfigure “a different regime of perception and signification” (Rancière, 2009, pp. 48, 49).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".