Bibliographic record
Abstract
Our intention in this inquiry was to imaginatively engage with data to see what may be made possible. Informed by the ontological turn (Zembylas, 2017) and a call for creative engagement with data (Wolgemuth et al., 2025), we leveraged cognitive tools (Egan, 1997, 2005), to playfully engage with interview transcripts. Despite approaching the inquiry from a relational, more-than-human ontology, we found ourselves continually constrained by humanist understandings. Our inquiry highlights how implicit and deeply-entrenched our assumptions about research are; we illuminate these assumptions by exploring iterative problematics that arose in our playful engagement. Our engagement in this inquiry allowed the data to work on us, as we worked on the data, producing us differently as researchers and as scholars. Our experience with imaginative methodology opened up possibility and proliferated the ‘what if?’ required to move beyond dominant ways of knowing. However, our experience demonstrates the tensions we continue to navigate as becoming-researchers.
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 | 0.088 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.044 |
| Scholarly communication | 0.034 | 0.061 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".