Reskilling for the Future of Work
Bibliographic record
Abstract
Due to economic, technological, and cultural changes, career paths whereby individuals move in and out of alternative working arrangements, build careers from hobbies, or transition into new occupations via non-traditional training programs are becoming increasingly common. While management scholars have developed rich theories on identity and skill development in external labor markets, we have less understanding of the pathways that shape discontinuous career transitions–transitions that entail major and simultaneous occupational and organizational changes. In this symposium, we focus on the tech sector as a setting for examining the nontraditional reskilling pathways that have begun to shape discontinuous career transitions, such as Massive Online Open Courses (MOOCs) and bootcamps. We raise the question of whether and how value can be more equitably distributed to employees and employers through new forms of training and labor market matching. We also examine how these new pathways–and the individuals that move through them–come to be recognized as legitimate by employers. We explore these questions by addressing both the supply and demand sides of the labor market and by examining multiple touchpoints in the training and hiring process. We begin by exploring employers’ sense-making around skill demands, shedding light on how skill requirements change in IT occupations. This motivates why new and alternative career pathways and training institutions have developed in response to rapid, demand-side change. We then discuss dynamics of knowledge development and job searching experienced by participants in these alternative pathways, as well as implications for our theories of occupational entry, learning, and socialization. The Occupational Vision: Examining Changing Profiles Within IT Occupations Author: Pedro Seguel; McGill U. - Desautels Faculty of Management Author: Lisa Ellen Cohen; McGill U. Author: Emmanuelle Vaast; McGill U. Fast-Skilling Communities and the Rise of Expert Assemblers Author: Dilan Eren; Ivey Business School Job Search Strategies of Nontraditional Job Candidates Author: Ece Kaynak; Bayes Business School (formerly Cass), City, U. of London Hustle or Happenstance? How Career Planning Tendencies Impact Discontinuous Career Changes Author: Jenna E. Myers; U. Of Toronto-Ind Rel Lbr
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".