A Comparative Study of AI-Powered Workforce Development via Forensic Analytics, Blockchain, and Metaverse
Bibliographic record
Abstract
This paper focuses on the outcomes of a Computer Forensics Summer Academy for High School Girls which was funded by the National Science Foundation over the 2018-2022 period. To overcome Covid-19 constraints, the project team adopted multiple content-delivery methods (in-person, hybrid, and virtual) to provide participants with career-exploration, job-shadowing, and professional-mentoring opportunities via information communication technology. Participants used artificial intelligence, blockchain, machine learning, metaverse, simulation, and virtual reality to analyze forensic data and solve simulations of modern-day crimes. Year-to-year comparisons revealed significant pre/post increases in participants’ career awareness, forensic knowledge, and technical competencies with the exceptions of career interests and motivation. These unanticipated results contribute new knowledge to the NSF’s comprehensive workforce model by examining how girls learn, work, and solve problems in varying multi-modality environments. As the learning space and workplace of the future evolve around human-computer technologies, insights on how to encourage STEM learning and workforce participation by under-represented populations become critical to better prepare today’s digital learners and build an equitable and innovative workforce via collaborative partnerships, career-exploration opportunities, and skill-acquisition venues.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".