A Comparative Study of AI-Powered Workforce Development via Forensic Analytics, Blockchain, and Metaverse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it