Embracing theories of precarity for the study of information practices
Bibliographic record
Abstract
Purpose The paper aims to explore the value of various notions of precarity for the study of information practices and for addressing inequities and marginalization from an information standpoint. Design/methodology/approach Several interrelated conceptualizations of precarity and associated terms from outside of library and information science (LIS) are presented. LIS studies involving precarity and related topics, including various situations of insecurity, instability, migration and transition, are then discussed. In that context, new approaches to information precarity and new directions for information practices research are explored. Findings Studies that draw from holistic characterizations of precarity, especially those engaging with theories from beyond the field, are quite limited in LIS research. Broader understandings of precarity in information contexts may contribute to greater engagement with political and economic considerations and to development of non-individualistic responses and services. Originality/value The presentation of a framework for an initial model of information precarity and the expansion of connections between existing LIS research and concepts of precarity from other fields suggest a new lens for further addressing inequities, marginalization and precarious life in LIS research.
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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.094 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".