Political fanaticism, alienation and the Trickster archetype in Guillermo Del Toro’s Pan’s Labyrinth and Terry Gilliam’s Tideland
Bibliographic record
Abstract
This thesis uses Jungian and post-Jungian methods of textual analysis as a methodological approach to analyze two fairytale films produced at the beginning of the twenty-first century, Guillermo del Toro’s Pan’s Labyrinth (2006) and Terry Gilliam’s Tideland (2005). Based on del Toro’s artistic rendering of the Spanish Civil War and Gilliam’s depiction of the Canadian prairies as an isolated and hostile landscape, it will be argued that political fanaticism and alienation are, from a Jungian psychological perspective, symptomatic of a modern mode of being that divests individuals of the numinous dimensions of existence. And since Jungian psychology formulates the numinous as a category of subjective experiences vis-à-vis the Self (Edinger, 1972), it follows, the thesis argues, that political fanaticism and alienation result from an attitude of neglect towards internal psychological processes and the unconscious. In this line of reasoning, the thesis further argues that the Trickster manifests in these films as a figure of remediation, which brings forth the importance of individuation as a means to rediscover the numinous through a dialogical relationship with the unconscious.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".