Universities and Industries: A Proactive Partnership Shaping the Future of Work
Bibliographic record
Abstract
Abstract Universities and Industries: A Proactive Partnership Shaping the Future of Work According to recent projections, 65% of current undergraduate students will be employed, upon graduation, in jobs that do not exist today [1]. Looking further ahead, 85% of jobs that will exist in 2030 have not yet been invented [2]. With the majority or future jobs unknown and their job descriptions undefined, how do universities prepare their students for successful careers in industry? Likewise, industries with evolving workforces are facing increasing pressure to identify and hire candidates who are well-suited to make an impact in these newly-defined positions. A recent study found that costs associated with searching, interviewing, offering, onboarding, and training a new employee costs a company 150% of the salary for the position hired [3]. In addition, retaining top talent is proving difficult for companies in a U.S. job market exhibiting recent lows in unemployment. The stakes are high for industry to find the correct employee “fit:” someone who can make a positive impact in a relatively short amount of time and grow with the organization for years to come. Companies are increasingly turning to university partners for help. Using the energy industry as case study, this paper will explore ways in which universities and industry are currently partnering to uncover the appropriate tools, identify desired skillsets, and determine the knowledge required for students to excel at future jobs whose descriptions remain undefined. Externally-facing offices in universities are engaging with corporate partners to identify disruptive trends in industry and ways in which their respective workforces will be impacted. The paper will highlight ways in which Worcester Polytechnic Institute (WPI) seeks to identify, communicate, and incorporate industry needs and ideas across departments in order to positively impact the education and opportunities available to students at all levels. Industry engagement allows universities to capture a more realistic, evolving career landscape for current students as they set their sights on the workforce. Works Cited [1] Global Agenda Council on the Future of Jobs. (2018). The Future of Jobs Report (Rep.). World Economic Forum. [2] Institute for the Future. (2017). The Next Era of Human/Machine Partnerships: Emerging Technologies' Impact on Society and Work in 2030 (p. 14, Rep.). Palo Alto, CA: Institute for the Future. [3] Swartz, M. (n.d.). How Much Does Onboarding New Employees Cost? | Monster.ca. Retrieved October 12, 2018, from https://hiring.monster.ca/hr/hr-best-practices/recruiting-hiring-advice/managing-hiring-costs/cost-of-onboarding-new-employees-canada.aspx
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.003 | 0.056 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".