Bibliographic record
Abstract
Most of my teaching career has been spent in American schools, most recently as a Teacher-Librarian at an English-Spanish elementary school. My international teaching career began in Qatar in August of 2012, when I started my new job as a Teacher-Librarian at a private K-12 school. My first year was spent rearranging the library’s collection and getting a feel for the school, its students and staff. By the end of the second term of the first year, I realized that the most important aspect of my job as a school librarian was going to be improving the literacy skills of my students. How to do this was my next problem and I immediately thought of the Battle of the Books (BOB) Program. My school district in Oregon had used it in seventeen elementary schools, both regular and bilingual. This was exactly what I needed because I was currently teaching in a bilingual school (English/Arabic). I went about getting support from my primary and secondary school teachers and administration. Once I had the support in place, I needed to take a closer look at how we had run the BOB Program in Oregon and then adapt it to my current situation. The things that I needed to consider in order to make the BOB Program a success were the following:1. Deciding which year levels would participate for the Primary and Secondary Divisions2. Selecting the reading levels for each division3. Deciding the number of books for each division to read4. Selecting the right books for the each division5. Making a Timeline6. Deciding the format of the questions7. Writing the questions8. Setting up the tournament9. Using Guest Readers during the tournament for each division10. Rewards for the winning teams of both divisions
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.219 | 0.118 |
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".