Finding yourselves in a changing world of work
Bibliographic record
Abstract
The career landscape is changing at an unprecedented pace. As a result, students in post-secondary education, and those that have recently graduated from post-secondary education, face tensions between the education system and real-world employment. Are post-secondary institutions sufficiently preparing students to navigate the changing work environment with the training and skills they acquired during school? This study explores “How might individuals discover future work aspirations during a conventional post-secondary course of study?”. \n \nWhile every person has the right to write their own story, dysfunctions in the social system inhibit our journey towards finding the right places where we can realize our full potential. For example, the lingering perception that post-secondary education is the default path to success and a curriculum that hinders self-reflection results in disillusionment among generations of graduates who find themselves stuck on a career path leading in the opposite direction than their actual goals. Fixing the dysfunctions can help alleviate the problem of 40.5% of graduates being unemployed and underemployed (CIBC). \n \nA gigamap is a visual design tool used to describe and illustrate the complexity of a wicked problem and the associated greater system in consideration with respect to the research question. The research team conceptualized this system as a pinball machine to emphasize the limits of control that youth have in finding a career in Canada. The eroding goals and shifting the burden archetypes and the multi-level learning loop diagrams are used to describe different reactions to the feeling of being stuck. Through this gigamap, we can begin to unravel the forces that move individual aspirations in hopes of generating insights that lead individuals onto the path of fulfillment. Thus, our wicked problem is the tension between inter-generational values, the needs of the changing world and the speed of the educational system adapting to this phenomenon. \n \nTal, B., (June 20, 2013) In Focus Tenth of Canadians aged 15 to 24 not employed or in school. CIBC Capital Markets.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".