Bibliographic record
Abstract
It's the off-season for my husband, but his work isn't over. The month of October is filled with playoffs, World Series games, and most importantly winter meetings, free agency, and finding a job. It's during this time that big-name players sign with teams for huge amounts of money. It's this time that determines our future and could change our lives forever. Our off-season excitement comes from celebrating our son's first birthday and our third anniversary, and spending quality time together again. We watch the World Series and dream what it must feel like to hear the cheers, to live the excitement of postseason play, but more importantly to prove what you can do for a team and earn your salary. We sold our home this June as a last effort to keep Bryan's big league dreams alive. It also eliminated the stresses of making a monthly mortgage payment on top of all the other costs that go along with this career. Many fans do not realize the costs that accompany being a professional athlete. Most players own a home, where they reside in the off-season, but are also responsible for the apartment they rent in the town in which they play. During spring training players do not receive a monthly salary but, rather, a daily allowance that is about enough to buy an extra-value meal at a fast food restaurant. Players often have to pay for their own living arrangements if they have a family and choose to live away from the team hotel. They are given meal money on the road, but incidentals throughout the season, such as food, laundry, clubhouse dues, and all moving expenses, are the player's responsibility. In 1998 we began dating. Bryan was in the big leagues with the Chicago White Sox. It all seemed so glamorous then, but I was still unaware that this lifestyle was not at all as it appeared. During that year we dated, I quickly came to know the truth about baseball and its hidden faults. On the outside the lifestyle was one that people would envy and wish they could be a part of; on the inside, it's one of the hardest things a family can endure. [End Page 110] I was first introduced to a sorority of sorts as I sat among a group of strong women. These women were the real muscle behind the players. People generally classify me as a "plain Jane," a jeans and T-shirt kind of girl. Some of the women were just like me, yet some were dressed in designer clothes, designer shoes, and carrying designer bags. I was introduced to Louis Vuitton for the first time when I noticed some of the wives carrying the bags proudly. I had never actually seen one up close, but now I noticed women were putting them under their seats, where peanut shells and hot dog wrappers were tossed. It was common to see Coach, Prada, and Kate Spade draping women's arms the same way I held onto my bargain purse. The women were impeccably put together from their heads to their manicured toes. I knew I was in a different class of women. I used to wonder how they could look so perfect through an entire baseball game in the middle of summer, while their hair and skin remained flawless. I noticed gorgeous jewelry that I had seen only in storefront windows. I was constantly watching "them" and not the game. It was unlike anything I had ever been associated with, and I wanted to know just how to fit in. Hierarchy During the first year of my relationship with Bryan, I realized I spent more time trying to "be" the same as the women I sat among and less time understanding what really went into an...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.000 |
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 teacher head, 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".