Information avoidance: A critical conceptual review. An Annual Review of Information Science and Technology (ARIST) paper
Bibliographic record
Abstract
Abstract Information avoidance has long been in the shadow of information seeking. Variously seen as undesired, maladaptive, or even pathological, information avoidance has lacked the sustained attention and conceptualization that has been provided to other information practices. It is also, perhaps uniquely among information practices, often invoked to blame or censure those who engage in it. However, closer examination of information avoidance reveals nuanced and complex patterns of interactions with information, ones that often have positive and beneficial outcomes. We challenge the simplistic tenor of this conversation through this critical conceptual review of information avoidance. Starting from an examination of how information avoidance has been treated within information science and related disciplines, we then draw upon the various terms that have been used to describe a lack of engagement with information to establish seven core characteristics of the concept. We subsequently use this analysis to establish our definition of information avoidance as practices that moderate interaction with information by reducing the intensity of information, restricting control over information, and/or excluding information based on perceived properties. We consider the implications of this definition and its view of information avoidance as a significant information practice on information research.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".