Quality Control for Quality Computational Concepts: Wrangling with Theory and Data Wrangling as Theorizing
Bibliographic record
Abstract
Sociologists encounter digitized data in all aspects of social life, albeit data of indefinite quality. We ask, how might scholars use the process of inspecting and cleaning such data as an occasion to advance social theory? We draw on Art Stinchcombe’s theory of formality to demonstrate how the craft of data wrangling can be used to assess and improve the quality of our concepts. Drawing on examples from contemporary computational social science, we show how the cognitive adequacy, communicability, and improvability of conceptual abstractions can be developed and refined in the data wrangling process. Using Stinchcombe to highlight the link between theorizing and the organizational and institutional systems in which such theorizing is embedded, we argue that the systems that impact the quality of our data simultaneously impact the quality of our concepts. Inspired by AI oversight research, we provide a roadmap for evaluating the systems involved in computational abstractions.
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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 | 0.178 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.007 | 0.114 |
| Scholarly communication | 0.029 | 0.060 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".