Description of the American Community of John Steinbeck’s Adventure in Novel Travels with Charley in Search of America 1960s
Bibliographic record
Abstract
This article aims at describing the social life of the American people in several places that made the adventures of John Steinbeck as the author of the novel Travels with Charley in Search of America around the 1960s. American people’s lives are a part of world civilizations that literary readers need to know. This adventure was preceded by an author’s trip in New York City, then to California, Connecticut, Rhode Island, New Hampshire, Massachusetts, Maine, New Jersey, Saint Lawrence, Quebec, Niagara Falls, Ohio, Chicago, Illinois, Michigan, North Dakota, the Rocky Mountains, Washington, the West Coast, Oregon, Arizona, New Mexico, Texas, New Orleans, Salinas, and again ended in New York. In processing research data, the writer uses one of the methods of literary research, namely the Dynamic Structural Approach which emphasizes the study of the intrinsic elements of literary work and the involvement of the author in his work. The intrinsic elements emphasized in this study are the physical and social settings. The research data were obtained from the results of a literature study which were then explained descriptively. The writer found a number of descriptions of the social life of the American people in the 1960s, namely the life of the city, the situation of the inland people, and ethnic discrimination. The people of the city are busy taking care of their profession and competing for careers, inland people living naturally without competing ambitions, and black African Americans have not enjoyed the progress achieved by the Americans. The description of American society related to the fictional story is divided by region, namely east, north, middle, west, and south. The social condition in the eastern region is dominated by beaches and mountains, and is engaged in business, commerce, industry, and agriculture. The comfortable landscape in the northern region spends the people time as breeders and farmers. The natural condition in the middle region of American is very suitable for agriculture, plantations, and animal husbandry. Many people in the western American region facing the Pacific Ocean become fishermen. The natural conditions from the plains and valleys to the hills make the southern region suitable for plantation land.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".