Expanded Model of Everyday Information Practices with Information Avoidance in Digital Environments
Bibliographic record
Abstract
ABSTRACT Until recently, research on information behavior and practices has focused on a series of actions, including having information needs, seeking information to satisfy those needs, and using the acquired information to varying degrees. However, in digital environments with an enormous distribution of information, it is necessary to consider information practices by focusing on their relationship with negative behaviors, such as information avoidance. Based on the discourses by Japanese Canadian seniors on information behavior during the COVID‐19 pandemic, this study attempted to construct an expanded model of everyday information practices (EIP) that incorporates the concept of information avoidance into the EIP model proposed by Savolainen. Findings suggest that information avoidance is likely related to an individual's social context and that, as a result of information avoidance, different means of information acquisition are chosen from a person's stock of knowledge, resulting in different aspects of the individual's information practices.
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How this classification was reachedexpand
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.035 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".