Strategies for Addressing the Lack of Skilled Workers in the Gig Economy
Bibliographic record
Abstract
In the competitive labor market landscape of 2023, skilled workers were in high demand, and owners of small businesses needed effective recruitment strategies to attract, recruit, hire, and retain skilled workers.Owners of small businesses who failed to implement effective recruitment strategies risked continuing a cycle of frequent employee turnover that created reduced productivity and loss of institutional knowledge and expertise, resulting in reduced profitability.Grounded in Winston's recruitment theory, the purpose of this qualitative multiple-case study was to explore the recruitment strategies owners of small businesses used to attract and hire skilled workers to achieve a competitive advantage and profitability.Six small business owners across Canada participated in this qualitative multiple-case study.Data were collected using semistructured interviews, literature on recruitment, available public sources of information, interview journal notes, and company websites.Using thematic analysis, five key themes emerged: (a) corporate culture, (b) organizational branding, (c) human resources management strategies, (d) advanced or disruptive technologies, and (e) retention strategies.A key recommendation is for owners of small businesses to establish a robust organizational employee recruitment outreach program locally or regionally by attending or hosting local skills events to attract skilled workers.The implications for positive social change include the potential for business owners to retain valued employees and lower community unemployment rates, which could enhance the economy and bolster the local community workforce.coach and became our Principal; he sat me down and explained the two-path road to life; his walloping advice influenced the rest of my life; thank you, Steve.To my second committee member, Dr. Denise Land, thank you for your support in taking over that position and reviewing, guiding, and
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".