Constructing Crime in a Database: Big Data and the Mangle of Social Problems Work
Bibliographic record
Abstract
This paper argues for programmatic change within social constructionist approaches to social problems by attending to materiality in the theoretical conception of social context. To illustrate how this might be done, we place the interplay between social problems construction and technology (what we refer to as the mangle of social problems work) at its center by examining how the advent of “big data” is impacting the construction of social problems. Using the growing field of intelligence-led policing (ILP) as our illustrative example, we will examine four effects the large scale collection and analysis of data has on the way social problems claims are made. We begin by arguing that big data offers a new method by which putative problems are discovered and legitimized. We then explore how large data sets and algorithmic data analysis are increasingly used for predicting future problems. Following this, we illustrate how big data is used to construct and implement solutions to future problems. Lastly, we use the interplay between big data and those who use it to illustrate “the mangle of social problems work,” where data is made meaningful and actionable through the interpretive and analytic processes of analysts and police officers.
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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 | 0.055 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 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".