Linguistic Personality of the Marvel Cinematic Universe Character Nebula: Narrative and LIWC Analyses
Bibliographic record
Abstract
Blockbuster films intended for an international audience often depict universally recognisable psychological motivations and limitations, presenting characters whose verbal expressions create distinct psychological impressions. This paper focused on the relationship between language use, including narratives and psychological categories of words, and the psychological traits of cinematic personalities. The study aimed to investigate how a character's language usage exposed their personality, psychological attributes, and transformations throughout their story arc and archetype. To achieve this, in the research a connection between Maslow's hierarchy of human needs, Jung's concept of archetypes, and Schmidt's typology of master characters in fiction was drawn, which expanded the understanding of the heroine Nebula from Guardians of the Galaxy verbal behavior. The research further defined the character's psychological image and arc by analysing their speech patterns. It identified the character's archetypes, namely the Backstabber and the Father's Daughter, and explored the progression between these archetypes. Additionally, the article employed the narrative analysis to construct the character's story. The narrative analysis was concluded with the implementation of the LIWC-22 psycholinguistic analysis in order to validate the findings derived from the psycholinguistics and cinematic studies. This method allowed for a comprehensive examination of the language used by cinematic characters, providing insights into their psychological traits and development. Ultimately, this research has contributed to the comprehension of how people's psychological characteristics are depicted and communicated through language in mass cinematography.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".