Bibliographic record
Abstract
SL stands for student librarians, and @Bibli8 is a short name for our library. This is about our routine yearly event. For the third year of our participation in International School Library Month (ISLM) 2012, we did several events. Some of them are common events that we always hold, and some others are new activities. Some new events were guitar clinic and Hangeul lesson. The guitar clinic involved a room with the guitar coach (which were students), the participants (which were also students) and me – as the moderator. This clinic facilitated sharing knowledge between students. Sharing knowledge between students could happen anytimeand anywhere. But here, I played a role as a mediator, who prepare everything for a great event. Being able to use a musical instrument is a life skill. I knew some students who knew how to play a guitar, and students who wanted to able to play guitar. Iarranged a time to facilitate a meeting between the two groups and they met in the library. The guitar clinic event was very successful, as the students were able to easily share their knowledge and learn how to play. The Hangeul lesson was similar in concept to the guitar clinic. In the Hangeul lesson, those who attended were students who are able to read and write in Hangeul words (Korean words) and those who wanted to learn that skill. These two events were new in my school. They were not expensive to facilitate, and the students were able to easily share their knowledge and learn from each other, enhancing their life skills, both for those who taught and those who learned.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.398 | 0.310 |
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".