Investigating Factors Related to Fear, Uncertainty, and Doubt (FUD) in End-User Cryptocurrency Behaviours
Bibliographic record
Abstract
Fear, Uncertainty, and Doubt or FUD, is relatively understudied in relation to cryptocurrency.It is a feeling derived from negative cryptocurrency-related information and it prompts adverse sentiment.This thesis addresses knowledge gaps on FUD by exploring its relationship with trust, and cryptocurrency information-seeking practices.We conducted 23 semi-structured interviews with cryptocurrency adopters and non-adopters to investigate triggers of FUD, FUD-induced behaviours, and how people form trust assessments of cryptocurrency information.Using thematic analysis, we classified FUD triggers found in our data across the personal, societal, and systemic level.Furthermore, we identified how participants make either cursory, extensive, or negative trust assessments of cryptocurrency information using attachment and depth.To illustrate this process, we proposed a model of trust assessment pathways.We then provide four recommendations on combating FUD, and suggest areas of future work.I am extremely thankful to my wonderful supervisor Dr. Sonia Chiasson.This thesis would not have been possible if not for her encouragement, guidance, and flexibility in helping me navigate a master's program fully online.I don't think I could have asked for a better mentor to work with and learn from.I am also very thankful for my co-workers at D2L and Visa -two wonderful places I had the pleasure of working at during my master's.I'd like to give a quick shout-
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".