Bibliographic record
Abstract
The text is a written rendering (heavily edited transcription) of the Sensing Salon held in Stockholm, on February 3, 2023, for the question: How to engage citational practices that support collaboration/make space within the institutional context? This collective reading is divided into three parts: (I) a Tarot reading done by the two of us, which includes explanations for the meanings of the cards, the positions, etc.; (II) a collaborative reading, which took the form of a free-flowing conversation, involving many if not most of those present; and (III) we close this documentation of the session with a reading of a Celtic Cross for one of the cards in the first spread, using a preliminary version of a deck we are in the process of developing, the Echo Tarot Deck*. The layering of readings allows for the complexity and nuance the question for this reading demands. As we zoom in with this reading, a glimpse of another image of citational practices appears. *The Echo Deck was inspired by the poems of African American-Japanese American poet Ai Ogawa. A provisional version of deck, with all the cards still blank, was first used in readings during our residency at Amant in New York, in 2022.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.040 |
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".