Database Ethnographies Using Social Science Methodologies to Enhance Data Analysis and Interpretation
Bibliographic record
Abstract
Abstract Data are the basis for many decisions ranging from assessing credit applications, to determining societal risk of criminals to adjudicating grant applications. Data collection and use constitute social practices, yet once data are placed in tables, their social lineage is forgotten. Database ethnographies are a unique means of using insights from science and technology studies and practices from the social sciences to enhance data analysis. The goal of this methodology is to elicit information from data stewards about the data in multiple‐use databases in order to provide an archive that describes the context and meaning of the data at a particular point in time. This article provides a review of a composite literature that contributed to the concept and implementation of database ethnographies. In addition, it illustrates how database ethnographies contribute to more nuanced metadata and act as the basis for informed decision‐making involving data from multiple sources.
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.
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 | 0.100 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".