Bibliographic record
Abstract
The intention of the WHY Lab is to foster a scientific approach to acquiring knowledge by encouraging students to observe the world around them. We are proposing an Introduction to Engineering course that will lead the prospective engineering students to discover their engineering vocation based on experiments representative of the engineering applications. To instill the idea that engineering is about "doing" and not just learning "equations, heuristics and theories". The Lab’s approach fosters a student confidence to design experiments and observe the outcomes. The new experiments become part of the catalogue of explorations. The new experiments focus on issues relevant to the student interests and keep the lab’s mission current.The internet and the pedagogy of engineering education has led to a system of accepted principles from which you could deduce an explanation for what you observed. Engineering program’s reliance on testing and homework results in codifying mathematics and scientific principles. Further, student reliance on the Internet to find facts, solutions, and generalizations, avoids the need for critical thinking or the use of an experimental approach. These factors lead to a rigid system with very little room for innovation or new thought.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.023 |
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".