Bibliographic record
Abstract
Mobile Homes Drive is the story of a mother, Nessa, raising a teenage daughter alone in a trailer park in the mid-nineties. When Nessa discovers neighbor Justin's dead child in a hot car, she finds herself amid a culture war. The town's sympathies do not extend to Justin, a recovering drug addict, though Nessa believes the child's death was an accident. At the front of the movement against Justin is Shelby Fuller, pastor of the local church and Nessa's boss. Nessa's testimony as witness to the incident is challenged as Shelby uses the child's death to kick off a bid for town mayor. Months later, participation in Justin's defense prompts Shelby to fire Nessa. Meanwhile, Shelby plans to demolish Mobile Homes Drive, where Nessa lives, to appease his constituents' prejudices against poverty. To legally outmaneuver the powerful clergyman, Nessa consults Justin's attractive attorney, Brent. Just as Nessa's new relationship blooms, her estranged husband Dale returns with a pregnant fiancé in tow. When Dale discovers an inappropriate relationship between his daughter and Pastor Shelby's adult son, Dale threatens jobless, homeless Nessa's custody of their daughter. Mobile Homes Drive contemplates the subtleties of a small-town class system, the role of Christianity in Southern politics, and the tangle of familial relationships.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.753 | 0.558 |
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".