Multilevel Analysis of New Year Resolutions
Bibliographic record
Abstract
Based on a literature research, we found plenty of different goal characteristics. However, these variables on which goals vary, which we call goal dimensions (Austin & Vancouver, 1996), were not measured with comparable items across the literature. Hence, we developed a comprehensive measure of goals by first extracting "common" goal dimensions (being measured in at least 10 publications and having found at least 10 items in the literature review). We then conducted a study asking participants 2-3 items from each common goal dimension and conducted an exploratory factor analysis resulting in 9 factors (which we named: Expectancy, Progress, Enjoyment, Support, Value, External Motivation, Demand, inter-goal Facilitation, & Commitment) with 3 items each. Then, we confirmed the factor structure by another study and confirmatory factor analysis. This resulted in a goal dimension questionnaire. Now, we want to investigate the multilevel structure of goals by a field study. In this study, we will ask the general population from the city of Siegen about their new year resolutions. Recruitment will be done a) in person at the "Kornmarkt", a public place in the center of Siegen, and b) via interviews given by Prof. Marie Hennecke and published in diverse newspapers and social media platforms from the University of Siegen. We introduce participants to the longitudinal study life and by the following homepage: https://neujahrsvorsatz.lwf.uni-siegen.de/. The measuring points are 30 days apart. Mails (incl. a 3 days reminder) will be sent by the platform "FormR". We want to analyse prevalent variance according to the levels: 1) variance of goals across time, 2) variance between goals and 3) variance in goals between persons.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".