Bibliographic record
Abstract
Boredom is a phenomenon that occurs in almost all individuals. Despite how common boredom is in our lives, little research has investigated whether boredom is quantifiable. We are using psychophysical techniques in an attempt to mathematically quantify boredom. We tested forty-two MacEwan undergraduate students who were classified as being high boredom prone (HBP = 15) or low boredom prone (LBP = 20). Participant performance was measured using Glass patterns, which require individuals to discriminate pattern from noise. Using these patterns, we were able to quantitatively extract various performance measures such as threshold, slope, error rate, and reaction time. We found that boredom proneness did not impact visual performance amongst HBP and LBP participants. We were unable to find significant differences between the two conditions when analyzing threshold, slope, error rate, reaction time, and the general psychometric function. These results suggest that boredom-proneness does not influence performance on psychophysical tasks, which in turn underscores the robustness of psychophysics as a measure of performance. Keywords: boredom, psychophysics, Glass patterns Discipline: Psychology Honours Faculty Mentor: Dr. Nicole Anderson
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".