Bibliographic record
Abstract
Who knew that fi tting a member for an FFA jacket could be so challenging? I was fortunate as a member to only have to buy one FFA jacket that fit me from my years in middle school until I re- ceived my American FFA Degree. Apparently the times have changed and during my time at Washington High School, I became the go-to for measuring students for FFA jack- ets. Let's just say we have Pam at the National Office on speed dial. While we have had successes in ordering jackets that fit our students, we have struggled with one student in par- ticular in getting her the perfect fit. Throughout several attempts to get the correct measurement, we ended up with a jacket that could be worn by many as a dress with three-quarter inch sleeves. Upon seeing this jack- et for the first time, many students commented that the jacket would be a perfect fi t for a tyrannosaurus rex. This sparked an idea in Mr. Cun- nien's head and we began to search for a t-rex that could fit the jacket. We kept the students out of the loop and one day when they walked into class there was a t-rex peering at them from the front of the classroom. The students instantly loved the idea and the Washington FFA now has a mas- cot: Titus the Tyrannosaurus Rex.I tell you this because some- thing that seemed small at the time has created an excitement within the Washington FFA Chapter. The stu- dents ran with the idea and wanted to have chapter t-shirts made next year that incorporate Titus. Mr. Cunnien's classroom is now filling up with t-rex models. We even awarded a member at the parent-member banquet with the Titus Award, which will be given annually to a member who displays enthusiasm for the FFA and bleeds National Blue and Corn Gold. There is even a friendly war being waged between the penguins at Jefferson High School and the t-rexes at Wash- ington High School. I am glad that time I was at Washing- ton High School that I could ex- perience a small idea that has grown like wildfire.I feel the same about my student teaching experience. Agriculture education was not my first career choice when I gradu- ated from college. I played college softball and earned a degree in Rec- reational Sports Management, but during the fall of my senior year I received my American FFA Degree and was during my trip to India- napolis that I realized I was not ready to say goodbye to the organization that had helped make me who I was at that moment. I talked to Dr. Boone, enrolled at West Virginia University and the rest is history. Like every- one else I was nervous about student teaching, but that nervousness lasted about two days into my placement at Charles Town Middle School. See- ing the enthusiasm that Mrs. Friend had about the subject matter she was teaching and the love she had for her students ensured me that I had made the right choice. As I began teaching my little 6th graders I faced the frus- trations that most teachers face with behavior and the students that do not really care at all what you are teach- ing. However, I was able to see the so-called light bulb come on and the students began to feel more comfort- able with me. By the end of my time there they had me watching Bubble Guppies (which is a cartoon similar to SpongeBob - the new it show for middle-schoolers). …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".