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Record W4200120993 · doi:10.1386/jsca_00051_1

Dream weaving and sonic metalepsis in Jan Troell’s Land of Dreams

2021· article· en· W4200120993 on OpenAlexaff
Alexis Luko

Bibliographic record

VenueJournal of Scandinavian Cinema · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNarrativeMusicalDreamFilmmakingAestheticsLiteratureVisual artsHistoryArtPsychologyMovie theater

Abstract

fetched live from OpenAlex

Jan Troell’s Sagolandet ( Land of Dreams ) (1988) presents itself as a documentary about 1980s Swedish society, but is also a film about filmmaking, the imagination, memory and autobiography. The film has multiple narrative levels: interviews, home movie footage, autobiographical anecdotes and imaginative sequences. Commentary and guiding themes are drawn from the theories of psychoanalyst Rollo May. These strata and themes have associated musical motifs and/or sound effects, which, as the film progresses, serve as an ontological bridge between the different strata. Land of Dreams is structured as both a multistrand and multiform narrative with the intercutting of multiple stories with multiple protagonists (multistrand) mixed with dream worlds and internal-subjective perspectives of Troell (multiform). The different narrative strata invite metalepsis, a type of narrative ‘transgression’ that occurs across the boundaries of distinct narrative worlds. In Land of Dreams , voice, music and sound effects act as metaleptic agents, transgressing different strata through four interrelated techniques: (1) metaleptic ‘i-voices’; (2) musical structures made up of ironic and disjunctive musical textures; (3) musical motifs transgressing narrative and ontological boundaries and (4) musical metaleptic warps. Musical metalepsis in Land of Dreams functions in a way that is emblematic of how political decisions and public policy infiltrate the private sphere, human consciousness and even dreams of the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.224
Teacher spread0.206 · 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 teacher head, not a consensus.

Study designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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