A metaBUS-enabled meta-analysis of career satisfaction
Bibliographic record
Abstract
Purpose In this study, the authors revisit the meta-analytic correlates of career satisfaction and demonstrate the use of metaBUS – a database repository of meta-analytic effect sizes and related information from the field of applied psychology. The purpose of this paper is to extend prior meta-analytic research on the topic of career satisfaction and compare the results from the metaBUS-enabled meta-analysis, with the results from meta-analyses that do not build on the repository. Design/methodology/approach A multilevel meta-analysis was conducted on all correlates available in the metaBUS database and the approach was described in a step-by-step fashion. Findings The demonstration reiterated some of the findings of prior meta-analyses, but also revealed considerable incongruity between the sample taken from the metaBUS database and the meta-analytic sample from studies that relied on non-metaBUS-based literature searches. Nevertheless, the results are similar in terms of the directions of the effects and the relative sizes of the effects. Research limitations/implications The paper demonstrates the use of the metaBUS database. In addition, results suggest that meta-analyses on career satisfaction might have suffered from sample selection issues, but further research is required in order to establish the source of the sample selection incongruence. Originality/value This is the first step-by-step demonstration of the use of metaBUS specifically for meta-analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".