Message from the conference chairs
Bibliographic record
Abstract
On behalf of the organizing committee for the conference ICDM 2017, we would like to extend our warmest welcome to all of you to the 2017 IEEE International Conference on Data Mining and to New Orleans-The Big Easy.Founded by the French in 1718, ruled by the Spanish for 40 years, and bought by the United States in 1803 as part of the historic "Louisiana Purchase", New Orleans is the largest city of Louisiana and one of the Gulf of Mexico's busiest northern port.New Orleans has been dubbed "America's Most Interesting City" and is famous for its Mardi Gras festivities, its ethnically diverse population, and its rich musical history and traditions-after all, it is the birth place of Jazz.New Orleans is the most European of American cities, a festive place, rich in history, culture and atmosphere.It is a famous tourist destination and visitors come from all over the globe to experience the old-world charm of its famous French Quarter, dine in its fine restaurants, listen to jazz, and browse its fine antique shops.New Orleans lives by its motto: "laissez le bon temps rouler!" (let the good times roll!).
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.097 | 0.090 |
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".