Intuitive and Reflective Foundations of Free Will and Scientific Determinism Exp. 2- Phase 2 data collection
Bibliographic record
Abstract
In this project, we attempt to replicate our previous study (see OSF link: https://osf.io/p7gnz/?view_only=4f5d2f2d795642f0957e1685e4a5342e). The experiment in the previous study yielded mixed evidence regarding the relationship between reflective cognitive style and scientific determinism. Contrary to our initial hypothesis that reflection would increase the endorsement of scientific determinism while decreasing the endorsement of free will, the research showed that reflection decreased support for both free will and scientific determinism. This unexpected finding allowed us to propose a novel hypothesis called the reflective doubt hypothesis based on Yılmaz and Isler’s previous finding (2019), showing that reflection increased belief in God for non-believers, but it tended to decrease it among believers. Yılmaz and Isler (2019) argue that reflection leads to an increase in doubt in one’s initial or intuitive judgments. Hence, we assume that the decrease in scientific determinism when using reflection could be a result of doubt. Hence, we aim to confirm this unexpected finding with improved methodology. This is Phase Two of our study, where we are collecting additional data from Canada. In the initial phase, data collected from Turkey did not reach the expected statistical power, resulting in an underpowered study. To address this issue, we are expanding our data collection to Canada, a WEIRD (Western, Educated, Industrialized, Rich, Democratic) country compared to Turkey. This phase aims to strengthen the study’s robustness by increasing sample size and cultural diversity. Importantly, we are not introducing any new confirmatory hypotheses; our research objectives and hypotheses remain consistent with the original phase.
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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 | 0.049 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".