An Investigation Into Predictive Variables of Materialism, Greed, Envy for the Student Body.
Bibliographic record
Abstract
The objective of science is to make sense of the world that surrounds us through fact and reason. When a study is conducted, results should undergo severe scrutiny before being published. A recent survey done at MacEwan University was conducted to determine if eating habits were influenced by envious or materialistic thoughts. Ultimately the study was unable to provide sufficient evidence for the hypothesis. The main conclusion was that priming questions administered in the study were unsuccessful at accomplishing their objective. More careful analysis of the data through categorical means shows that this may not be the only downfall of the study. This paper provides a further in-depth analysis that provides confirmation of the aforementioned statement as well as suggestions to improve the study design. A larger sample is recommended. One study showed promise for demographics that appear to be affected by priming questions. Suggestions are also made for different methods of priming. It is recommended that the original study be repeated to see if changes to the design still fail to provide sufficient evidence of an association between eating habits and materialistic/envious thoughts. Discipline: Statistics Faculty Mentor: Dr. Karen Buro
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".