Bibliographic record
Abstract
ILC Communications Workshop -- Vancouver July, 2006 \n\nThe LCSGA Communications Committee is planning a half-day workshop for the afternoon of July 18, 2006 in conjunction with the Vancouver Linear Collider Workshop. The goal of this workshop is to understand and come to agreement on a inspirational, consistent, credible, sustainable and differentiating ILC communications strategy for building a consensus of support for the ILC both inside and outside the HEP community. \n\nILC scientists and staff will be giving talks and communicating with scientific (both HEP and non-HEP) audiences. These talks must be clear, credible and consistent while demonstrating purposes and importance of the ILC.\n\nWorkshop outcomes include clarifying primary ILC audiences and needs, communications goals, obstacles to successful communications, and mitigation strategies to ensure we achieve our communication goals. A pre-workshop survey will be distributed to participants. Please reply as it will significantly help us prepare for the workshop. \n\nSubsequent to this workshop the communications team will develop and maintain up-to-date presentation outlines, tools, materials and resources to assist ILC presenters throughout the world. \n\nThe LCSGA Communication Committee members are Jonathan Bagger, Jim Brau, Neil Calder, Judy Jackson, Ritchie Patterson, and William Trischuk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.377 | 0.223 |
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".