THE IMPACTS OF GOLD FEVER ON SOCIAL CONDITION IN \nNORTHWEST TERRITORIES OF CANADA IN THE LATE OF 1890’S AS \nREFLECTED IN JACK LONDON’SWHITE FANG
Bibliographic record
Abstract
The second reason is this novel describes the effect of gold fever into the life \nof society in Klondike. In White Fang, there are many people race from US to \nachieve the gold at the Northwest Territory in Yukon, Canada at the late 1890’s. \nThe travelers are blinded by the gold fever. However, that territory is dangerous \nand fifties below zero freeze. Birdsal and Florin in their book Garis Besar \nGeografi Amerika : Lanskap Regional Amerika Serikat states: \nSifat lingkungan fisiknya yang tidak ramah, ditambah dengan \njarangnya pemukiman, merupakan karakter khusus Northlands. […] \ntemperature Januari rata – rata berkisar dari yang tinggi sekitar -7oC \nsepanjang tepi Great Lakes bagian selatan sampai -40oC, di sebagian \nAlaska temperatur dapat mencapai -60oC. (170) \n(Physically with harsh environment and rare residences are the \ncharacteristic of Northlands. […] in January its temperature from the \nhigh scales is -7oC as long as Great Lakes edge in south until -40oC, \nand -60oC at a part of Alaska.) \nJack London describes those phenomena in this novel by using the narrative \nstyle that show the social condition on Klondike in the Northwest Territory of \nCanada’s Yukon as the impacts of the power of gold fever which is influences to \nreach the welfare society. Based on the two reason mentioned above, the writer is \ninterested to analyze this novel and decides to entitle this research with “The \nImpacts of Gold Fever on Social Condition in Northwest Territories of \nCanada in the Late of 1890’s As Reflected in Jack London’sWhite Fang”.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".