UNESCO "Training the Trainers in Information Literacy Workshop": Announcement and Call for Participants
Bibliographic record
Abstract
Welcome to the 11th European Conference of Medical and Health Libraries. Towards a new information space: innovations and renovations, Helsinki, Finland, 23rd - 28th June 2008. The Finnish Medical Library Association, Bibliothecarii Medicinae Fenniae (BMF), in collaboration with the National Library of Health Sciences, has the great honour and joy of inviting you to the 11th EAHIL (European Association for Health Information and Libraries) Conference. The EAHIL 2008 Helsinki conference offers a high quality scientific program. The proceedings will cover many interesting and current themes, especially the following topics inspired the authors: virtual communities and virtual libraries, evidence-based practice, education and professional development and new technologies and applications. In addition, a number of inspirational continuing education courses will be offered. Registration deadline for early birds: March 31, 2008 (380 €) Registration: April 1, 2008 onwards (430 €) Accompanying person: 230 € Continuing Education Courses: 60 € - 80 € Registration: http://www.congreszon.fi/eahil_2008/registration/ Please visit the EAHIL 2008 Helsinki home page http://www.congreszon.fi/eahil_2008/ for information. For current discussions about the conference, please visit the EAHIL 2008 Helsinki blog at http://eahil2008.blogspot.com/.
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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 | 0.009 | 0.007 |
| 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 | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.197 | 0.084 |
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".