Sharing Data, Repairing Practices: On the Reflexivity of Astronomical Data Journeys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it