Ipar Haizearen Erronka: A boat trip from the Basque Country to Newfoundland
Bibliographic record
Abstract
The nature of animated cinema involves the creation of any realistic or fantastical characters, places, and situations. Animation can be used to take characters far from their hometowns on believable journeys without big budgets used on location shooting. The Basque animated feature film Ipar Haizearen Erronka (The Challenge of the North Wind), directed in 1992 by Juanba Berasategi, illustrates how animation can represent a journey and a historic reality in a plausible way. The movie depicts a Basque whale hunting vessel travelling to the wild coast of Newfoundland, Canada in the sixteenth century. Typically, Basque live action movies in the 80s would recreate foreign locations with nearby settings. Ipar Haizearen Erronka avoids this problem by showing America through drawings. In this paper, we will use the movie Ipar Haizearen Erronka to interpret how animation uses backgrounds and objects to represent a voyage across the Atlantic Ocean and determine the realistic accuracy of the social and historical moments represented in the movie. We will also see how this journey embodies the characteristics of the literary genre of Bildungsroman, as well as the narrative structures pointed out by Vladimir Propp’s folktale and Joseph Campbell's monomyth. The study also focuses on how the film depicts the most representative characteristics of the journey, and how they are used as filming narrative resources. A closer look will be taken into the main vessels, the captain's logbook, the map, the historical context of the sailing of the ship, the maritime laws where sexism is abundant, the financing of the trip, and the work on board.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".