Help Your Gig Workers Become Their Best Selves: A Self-Actualization Action Framework
Bibliographic record
Abstract
With organizations moving towards a non-traditional setup, the gig economy is booming like never before. However, the psychological needs of gig workers are often overlooked and thus limited research has been observed in this area. This study explores the importance of fostering self-actualization among company gig workers to increase their productivity, motivation, job satisfaction, and organizational commitment. The purpose of this study is to shed light on why and how organizations should be concerned with drawing in and keeping gig workers who want to realize their full potential by allowing them to do so on the job. Employees who choose to actualize themselves would be happier and more driven, and this would have a significant impact on the calibre of their work with client teams and companies. For this study, a self-developed survey instrument has been used to gain insights into the job satisfaction levels of gig workers. The results indicated that gig workers strongly believe that they are not adequately oriented on company values and long-term strategic plans and that most client companies are unconcerned with providing career track plans to gig workers or upskilling opportunities. The authors, therefore, propose an actualization checklist and framework that would serve to guide managers in retaining and engaging with gig workers to improve their task, team, and organizational engagement and job satisfaction.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".