Bibliographic record
Abstract
DEPARTMENTS AND COLUMNS AT DEADLI E COMMENTARY EDUCATION CORNER ON THE MARKET CLOSE-UP COMPUTERS/SOFTWARE MANAGING LAW GETTI G ORGANIZED EMPLOYERS GROUP CORPORATE PROFILE FINA CIAL SECOND PAGE ONE WINE REVIEW EXECUTIVE TIME OUT STATLER'S BEST BETS RESTAURANT REVIEW MANAGER'S BOOKSHELF NEW BUSINESS NEWS AND FEATURES La Quinta "Stakeholders" Break Ground Consolidating California’s Energy Bureaucracy Former Capitan John Magness Adapts Pilot's Skill s First Lady invites CSUSB Professor to White House CSUSB Professor Edits New Journal Faces in Business Celebrating We Tip's 30th Anniversary Orchard's Market to Open New Location Rosy Employment Picture for Ontario/Upland Building a Retirement nest Egg for Two Investors Must Consider Options Price WaterhouseCoopers Launches Campus Campaign Great Hospitality Begins With a SMILE Lessons in Etiquette Codding to Head Up Tournament Operations
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.639 | 0.536 |
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".