The Critic as Designer: How Metacognition Makes Transdisciplinarity Possible
Bibliographic record
Abstract
Gray receieved his B.S. in Electrical and Computer Engineering from Virginia Tech in 2000.He then earned a M.S. and a Ph.D. in Materials Science and Engineering from Virginia Tech in 2002 and 2010, respectively.Much of his graduate education focused on semiconductor devices physics and materials processing.However, his actual Ph.D. dissertation was on thermal modeling and process control of a friction stir fabrication method of additive manufacturing.Dr. Gray followed up his Ph.D. with a position as a post-doctoral associate under the guidance of Dr. Dwight Veihland working with composite magnetic field sensors.After his education, Dr. Gray continued his research in small-business environments, developing technologies and products across a wide range of fields including magnetic materials, sensors, and devices, energy harvesting technologies, harsh environment sensing, additive manufacturing, non-destructive inspection and evaluation, and vehicle autonomy.Dr. Gray came to the Engineering Education department as an instructor in 2018, and was promoted to Associate Professor of Practice in August 2019.Dr. Gray is primarily focused on pedagogy of first-year engineering students, but maintains an undergraduate research group with interests in automotive systems, communications, computing, and non-destructive inspection.
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.
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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".