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Geographical space and cinema images of the white Arctic on a white screen (Part I)

2023· article· en· W4365150344 on OpenAlexaboutno aff
I. S. Zonn

Bibliographic record

VenuePost-Soviet Issues · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticMovie theaterTundraSnowWhite (mutation)GlacierGeographyArchipelagoGeologyOceanographyHistoryPhysical geographyArchaeologyArt historyMeteorology

Abstract

fetched live from OpenAlex

The infinity of spaces, open spaces, the “White Silence” of the classical Arctic with a harsh climate, with its ice-bound Arctic Ocean, polar seas, archipelagos, islands covered with glaciers and snow, rocks breaking off into the sea, permafrost has always been a hostile environment, causing human fear. The icy Arctic desert on the shores turns into hard-to-reach areas of tundra, forest tundra and northern taiga. These are Siberia, Alaska, the Canadian Arctic archipelago, the Scandinavian-Icelandic polar regions. But it was their ignorance, their natural riches of furs, fish, forest, gold, and later oil and gas that attracted attention. With the advent of cinema, the Arctic and the North Pole crowning it, actually frozen ocean water, became the object of cinematographers. The whiteness of snow and ice refers us to the white sheet screen. The “white” polar nature appeared on it, affecting the destinies of people living in it, and those who came to master and transform it. The creators of cinema groped their way as well as the conquerors of the North Pole. At the beginning of the twentieth century . world directors Georges Milies, Bester Keaton, Lev Kuleshov, Charlie Chaplin have trodden the fantastic-comedy and dramatic silent cinematic Arctic path with their films. Then there were expedition documentaries, because the operators in the composition of the expeditions had to capture the historical events taking place in truly Arctic conditions, and a little later, feature films sometimes with world movie stars, in which, in accordance with the natural conditions of movement, new performers were introduced — animals, in particular dogs, deer, bears. The image, sound, and movement helped to enhance the demonstration of the color of Arctic landscapes and subjects on a white screen. Gradually, the Arctic occupied its niche in the panorama of world cinema not only as a decoration, but also as a full-fledged socio-geographical “actor”. However, the geography of film production of Arctic films is quite limited, although more than a hundred have been shot, which is explained by the few countries that surround the Arctic Ocean. The Arctic belongs to five states forming the Arctic Council — Russia, USA, Canada, Denmark, Norway. The article examines the history, genres, plot-thematic aspects of Arctic feature films.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.002

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.

Opus teacher head0.023
GPT teacher head0.316
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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