Self-Set learning goals and service performance in a gig economy: A Moderated-Mediation role of improvisation and mindful metacognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing on goal-setting theory, the current research examines whether the indirect relationship between self-set, rather than assigned or participative, learning goals and an Uber driver’s service performance is positive and significant in an emerging work context, namely, the gig economy. In this regard, we hypothesized that there is a positive, significant relationship between self-set learning goals and a driver’s improvised ways to provide customer service. Building on metacognitive practice, we further hypothesized that a gig driver’s mindful metacognition positively moderates the relationship between improvisation and service performance. The overall hypothesis tested is that the indirect relationship between self-set learning goals and a gig driver’s service performance via improvisation is positive and significant, and this relationship is positively moderated by mindful metacognition. Data were collected from 149 gig drivers. Ordinary least squares regression-based path analyses revealed support for these hypotheses.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it