Bibliographic record
Abstract
Inspired by the carrier bag theory of fiction let us look for alternate ways to do our work. We do this by worlding: by attuning to and melting into the subject(s) of our research; gathering and gaining situated knowledges, interwoven with multiple threads of imagination and desire. But then gathering and gaining is based on collecting: data, objects, subjects, situations, relations; submitted into an order of things, shifted into storages, from time to time put on display. Imagination and desire are stripped off in this process and stored separately, if at all. That’s why we need a different mind-set, a different set of methods, and a different set of tools. For our findings and our creations, for our research and our inspiration we will build a wunderkammer. Not the old cabinet of curiosities based on items taken away from others, other places, stolen from life. But a new structure for our wild-at-heart pluriverse; one that is our workshop and our toolbox rather than a storage. One that is probably closer to an assembly, a parliament, a party, a network, a collaboratory for all kinds of agencies and for agencies of all kinds.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".