A Case for Critical Data Studies in Library and Information Studies
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
The proliferation, ubiquity, and growth of data, big data, and digital infrastructure raise a number of questions for library and information studies (LIS) practitioners, researchers, and educators. While some uncritically accept and embrace the idea that big data will fundamentally alter every sector of society including economics, politics, health care, and knowledge production, others are more critical of the data turn. Data can be contradictory in that it can be used for surveillance, impinge on privacy, be used for secondary purposes (often without consent), and can be totalizing in that we continually create data exhaust, it can be hacked, searched, aggregated, and preserved for years. Conversely, data can be used for the public good, to promote progressive social change, and to empower people. The overarching argument presented in this paper is that critical library and information studies must include critical data studies. To develop this argument, this paper explores the ontological nature of data and their contradictory implications and effects in terms of broader society, the academy, and in LIS research, education, and practice. Next, the philosophical foundations and the work being done in the budding area of critical data studies are presented (most notably work by Rob Kitchin). Finally, the intersections between critical data studies and LIS are discussed in terms of research methodologies, philosophical underpinnings, and application of critical social theory, values, and ethics using Dalton and Thatcher's seven data criticisms.
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 | 0.001 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.164 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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