Diffractive Respondings and Cutting Together-Apart: Toward More-Than-Human Academic Community
Bibliographic record
Abstract
In this “paper,” we share our process of exploring the possibilities for the emergence of new ways of knowing-thinking-doing response-able collaboration. In an effort to come “together-apart” as author-collaborators, working from our different positionings and locations in the world, we shared text and images of the various ways the ideas from individual papers (nodal points) and our diffractive processes were moving (with) us. Sharing these creative respondings, new relations co-emerged through the texts-images and the various diffractive patterns that traveled widely through screens, devices, bodies, from New Zealand and Australia, to Iran, London, Finland, Canada, and the West and East Coasts of the United States. Paying attention to the fine details, difference emerged, as did new forms of more-than-human connection. The visual and written respondings were then cut together-apart (literally, metaphorically and methodologically) to represent the multiplicities of the diffractive process, bodies, hauntings, absences and excess, and the tensions and affective vulnerabilities that co-emerged through our process. We present four visual montages of the diffractive patterns that surfaced from the individual papers as nodal points and our creative collaborative processes of becoming-with the special issue. We conclude with some final thoughts on the process of diffracting the special issue, inviting the reader to join us in imagining new lines of flight, alternative possibilities for becoming a more response-able, more-than-human academic community.
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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.026 | 0.071 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".