Sharing Data, Repairing Practices: On the Reflexivity of Astronomical Data Journeys
Bibliographic record
Abstract
Abstract This chapter probes into how scientists’ discursive interactions are oriented not only to others’ arguments but also toward achieving an agreement on what data are like and how they ought to be used. It does so by attempting a reading of an episode of data re-use from recent astronomy that is mindful of researchers’ interactional and discursive work. I focus on the presumed detection, in 2004, of a galaxy at record distance from Earth. The original data became public at the time of publication and were soon re-used and supplemented with new observations by other teams. Data re-using scientists sought to reconstruct the practices used in making the discovery claim, and found them at fault. This allowed them to suggest the repair of data and of data use practices, which were subsequently taken up by the scientists who had claimed the discovery. I argue that this work was enabled by astronomy’s discipline-specific architecture for observation, of which objectual, technological and institutional elements provide contexts and resources for achieving the reflexive repair of data and data use practices. These astronomers experience data journeys more as reflexive loopings in screen-mediated work than as itineraries across physical sites or geographies.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".