Working data together: The accountability and reflexivity of digital astronomical practice
Bibliographic record
Abstract
Drawing on ethnomethodology, this article considers the sequential work of astronomers who combine observations from telescopes at two observatories in making a data set for scientific analyses. By witnessing the induction of a graduate student into this work, it aims at revealing the backgrounded assumptions that enter it. I find that these researchers achieved a consistent data set by engaging diverse evidential contexts as contexts of accountability. Employing graphs that visualize data in conventional representational formats of observational astronomy, experienced practitioners held each other accountable by using an 'implicit cosmology', a shared (but sometimes negotiable) characterization of 'what the universe looks like' through these formats. They oriented to data as malleable, that is, as containing artifacts of the observing situation which are unspecified initially but can be defined and subsequently removed. Alternating between reducing data and deducing astronomical phenomena, they ascribed artifacts to local observing conditions or computational procedures, thus maintaining previously stabilized phenomena reflexively. As researchers in data-intensive sciences are often removed from the instruments that generated the data they use, this example demonstrates how scientists can achieve agreement by engaging stable 'global' data sets and diverse contexts of accountability, allowing them to bypass troubling features and limitations of data generators.
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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 | 0.119 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.132 |
| Scholarly communication | 0.024 | 0.039 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".