Bibliographic record
Abstract
Eastern Illinois University is a community enthusiastically engaged in intellectual exploration. This includes faculty testing the boundaries of knowledge within their disciplines and the boundaries of how to convey that knowledge to their students. It also includes students, both graduate students joining with their faculty mentors in pressing the disciplinary boundaries, and undergraduate students discovering the front edges of the disciplines under the guidance of their instructors. Today we celebrate the full range of such faculty contributions: journal articles, book chapters, monographs, art works, music compositions, performances in all forms, and a host of others. Funded research contributions also are included and span similarly the full range just listed above. New this year is recognition of special contributions to pedagogy using technology – the Hoadley award for innovative and cutting edge contributions in this form. As always, I invite each participant at today’s event to browse the contributions of your colleagues, ask questions, and celebrate the intellectual vitality of this wonderful university. We have had plenty to lament during the past year, but today is a day to recognize and appreciate the vibrancy of our university and community of scholars. This is tangible evidence of why we went into this career. Provost Dr. Blair Lord
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.732 | 0.461 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".