All-Encompassing Skill Portal for Skills Management and Development
Bibliographic record
Abstract
Abstract One of the challenges for new engineering graduates is to find the dream job, and one of the challenges for industry is to find the right engineer to hire. Graduates will have to apply for many companies hoping to get interviews that will give them an opportunity to sell their skills and get the job. On the other hand, companies will have to interview many candidates hoping that they will get the right candidate to offer him/her the job. This is a tedious, time consuming, and costly process for both the industry and the new graduates. Many efforts are done by universities to offer certain general specialties to meet the industry needs. However, changes in universities curriculums are constrained by the curriculum requirements for the offered degrees. Also, curriculum changes are long processes, and by the time the changes are implemented, the industry needs may have already shifted. -Universities are educating students to have a solid skill set and inspire them to be life-long learners, and the companies can provide the training on the job for their new employees to further develop their skill sets. The authors of this paper propose building a skill portal with all three stakeholders in mind: Students (future engineers), Industry (potential employers), and Academia (educators of the potential engineers to be employed by industry or other sectors). The skill portal will allow the industry (company) to enter their desired skills via a GUI (Graphical User Interface) and save it into a database; allow students to view needed skills per different companies, and the university will design programs and activities (certification programs, in particular, given its flexibility and the potential to integrate it with some courses) to prepare students with the needed skills for certain industry needs. A pilot program is now running to solicit needed skill from the industrial partners of our university, and to encourage the students in a class to pursue a certificate, with both pieces of information to be shared among all the relevant parties. The pilot program will be used to evaluate this program's impact on shortening the time for the students to get the right job after graduation, and the time for the companies to hire the right employee.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".