Bibliographic record
Abstract
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".