Freelance job search during times of uncertainty: protean career orientation, career competencies and job search
Bibliographic record
Abstract
Purpose Freelancers are a growing population of working adults with limited to no organizational support. Yet, their strategies to navigate job search, especially in turbulent times, are unknown. To address this gap, the author hypothesized and examined a sequential mediation model whereby freelancer protean career orientation (PCO) influences job search strategies through career competencies (i.e.knowing why,howand withwhomto work) and job search self-efficacy (JSSE). Design/methodology/approach Data were collected from a sample of 87 Canadian freelancers during the height of the COVID-19 pandemic. Findings The results supported the sequential mediation from PCO to job search strategies through two of the career competencies (knowing whyandhow) and JSSE. The mediating role ofknowing whomwas not supported. Practical implications Policy makers and learning institutions can provide freelancers with opportunities to develop transferable skills through massive open online courses (MOOCs). Employers of freelancers can design motivating jobs that provide freelancers with on-the-job learning and development opportunities. Social implications The insignificant mediating role of knowing whom (i.e. professional networks) implies that large networks might not be necessarily beneficial in times of crisis. Thus, the role of networks might be more complex than the literature has proposed. Originality/value This study brings into focus an overlooked population of workers: freelancers. It investigates a sequential mediation through which freelancer PCO impacts job search strategies. In addition, it compares the effectiveness of career competencies in unfolding the proposed sequential mediation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".