A refractory engineering program for the 21 st century
Bibliographic record
Abstract
The Federation for International Refractory Research and Education (FIRE) has been conceptualized twenty years ago. At the time, its purpose was to maintain the training of graduated engineers to enroll and to adapt to the new business plans in the refractory industry. It was the blooming of the Information Age, of the knowledge workers. This paper is about the need to adjust to a new era, knowing that the benefit of education prime value is its long-term value. To have an outer and an inner vision about innovation, the first part of the paper is concerned about how do we learn and how our brain rules. In the second part we try to anticipate the customers’ needs trying to surf with them on the Ecology wave, including the Environment, Energy, Economy, and Ethics other waves. Accepting that this is already brewing at an accelerated rate, the conclusion is that FIRE and the other educators need to continue mimicking the CDIO (Conceive Design Implement Operate) approach which has inspired us for the last 20 years, for another 20 years, to adjust to the Conceptual Age in order to educate the creators and the empathizers who will direct the flow, in the refractory industry.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.101 | 0.036 |
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".