PERSONAL INFORMATION ARCHIVING: BEHAVIOURAL RESPONSES TO THE PERCEPTION OF RISK
Bibliographic record
Abstract
The paper investigates the factors that influence perceptions of online risk and the consequential behavioural responses to those perceptions. Using Bates’ theory of information behaviour, we focus on online protection strategies and digital archiving as a specific instantiation and manifestation of information behaviour and analyze how factors, such as perceptions of online risk and self-reported internet skills, have consequences for information behaviours. The study uses semi-structured interview data (n=101) collected from East York, Toronto. We asked about individuals’ perception of risk online, self-reported internet skills, protective measures when going online, and digital archiving practices. Our findings identify a nuanced relationship between perceptions and behaviours. The results offer an alternate perspective on online information behaviour that departs from traditional classifications that rely on _demographics_. We offer a refinement to the definitions of information behaviour by Bates (2010) and Fisher and Julien (2009) to include factors that modify behaviours, and develop a user typology relating specifically to perceptions of risk online.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".