Bibliographic record
Abstract
How do we do our work as scholars in an age of electronic reason and computational media and under media-saturated, algorithmic conditions? In this article I suggest that the age of electronic reason, the ubiquity of computational media, and our condition as algorithmic are not only valid objects of study for humanists, digital humanists, and post-humanists today. As scholars, we are also and always/already affected by these so-called objects. We live and work with/in them, a situation that has methodological implications. By visiting concepts and arguments of thinkers like Rosi Braidotti, Donna Haraway, Achille Mbembe, and Isabelle Stengers, I ask: how not to be indifferent to knowing that algorithms repeat age-old patterns of in- and exclusion? How to act on possibilities for change as critical and creative researchers? How does research worthy of our time look?
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 | 0.080 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".