Bibliographic record
Abstract
Doctoral education often treats academic writing as a solitary, human-centered activity, guided by conventions that emphasize structure, clarity, and discipline. These frameworks rarely consider how other-than-human entities shape the writing process. This article explores how multispecies assemblages inform doctoral writing, proposing that knowledge production can be understood as an eductive process – an unfolding of latent ideas through relationship with the so-called “natural” world. Drawing on examples from my own work, I share an excerpt from a multispecies duoethnographic project that seeks to recognize and incorporate other-than-human perspectives. I reflect on how these encounters have shaped my scholarly voice and academic identity, challenging dominant assumptions about writing as an isolated human endeavor. Reimagining writing as a relational, evolving practice, I offer reflections for integrating multispecies sensibilities into doctoral training and invite educators, researchers, and students to view academic writing as a collaborative process shaped by entanglements of human and more-than-human life.
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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.020 | 0.064 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".