Panel: Industry Relationship Development
Bibliographic record
Abstract
The purpose of the industrial panel is summarily stated as follows. This panel serves the goal of finding ways to increase industrial participation in the SMC Transaction journals. Only through industrial collaboration can the SMC society maintain financial solvency and an influx of industrial and academic minds into its constituent fields. It is our hope that the industrial panel will provide vision and direction with these ends in mind. Invitees to the panel currently serve or have served as a CEO, president, vice-president, or other ranking executive of a corporation or federal agency that has a vested interest in transitioning research; or, as a dean (any subrank), provost, president, or grant agent of an accredited North American Research University that has experience in marketing patents, or other research products such as software, etc. Several distinguished university professors have also be invited. They have special insight into technology transition, and have a track record of bringing in funding, as appropriate, publication (not necessarily extensive, which helps us to bolster our IEEE Transaction journals as one of the subjects of our planned deliberations). The panel will address, as its primary focus, how to increase the market share of our IEEE SMC Transactions (especially Part C), while maintaining or improving their quality, maintaining or improving their financial solvency, and publishing research that industry and government need done and can share in an open forum.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.258 | 0.112 |
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".