Gratitude to God - Cross-Sectional, 2021
Bibliographic record
Abstract
Participants and Procedure: There are 493 participants from the United States collected through Prolific Academic. Average age was 34.63. Gender included 43.0% men, 54.8% women, 1.8% nonbinary, and .4% preferred not to say. For ethnicity, 64.9 were White, 12.8% were Asian, 11.2% were Black, 7.7% were Hispanic, .8% were Middle Eastern, .4% were Native American, and 2.2% reported another ethnicity. For religious affiliation, 28.2% were Catholic, 19.3% were Protestant, 14.4% were Evangelical Christian, 13% were an "Other" religion that was typical of a more specific denomination of Christianity, 9.1% reported no formal religious affiliation, 4.7% were Agnostic, 3.4% were Jewish, 3.4% were Buddhist, 2.8% were Atheist, 2.6% were Islamic, and 2.2% were Hindu. Measures: Participants were asked questions about gratitude to God, a romantic partner, a friend, parent, and mentor. They were also asked follow up questions about their motivation to pay gratitude to these targets back or forward, and their relationship with each target. Other measures include political orientation, religious devotion, religious behavior, prayer motivation, prosocial behavior and motivation, motivation to develop character, Big 5 personality, self esteem, depression, anxiety, meaning in life, life satisfaction, awe, and other emotions (DES). The study was approved by the University of Toronto undergraduate ethics board.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".