Skills for the 21st Century: A Meta-Synthesis of Soft-Skills and Achievement
Bibliographic record
Abstract
Higher education can be both memorable and a vital pathway to the workforce. However, entering post-secondary life with the cognitive ability to handle the academic rigor is often not enough to succeed and persist in an environment that requires students to also possess soft-skills such as resilience, adaptability, perseverance, self-advocacy, and self-regulation (Adams, 2012; Cunha & Heckman, 2007; Egalite, Mills, & Greene, 2016). Therefore, this meta- synthesis sought to gain a better understanding of soft-skills deficits in adult learners by synthesizing current Canadian studies on the topic. It was found that interventions in higher education that resulted in soft-skills acquisition among learners were commonly geared toward graduate students and tied to social interactions among community agencies, faculty members, and peer groups. Thus, further research is discussed around examining the reciprocal effects of peer-mentoring on the soft-skills development of first-year undergraduate students, as well as the long-term impact this approach might have on student retention, achievement, and success beyond higher education.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".