Defence Mechanism in The Purpose of Colin Reeds’ Imaginary Companion in Finding Paradise Game
Bibliographic record
Abstract
This thesis examines the main character of Finding Paradise (2017) game, Colin Reeds, directed and written by Kan Gao, and produced by Freebird Games Canada. The purpose and significance of this research are to understand, analyse, and explain the main character’s personality through Freudian psychoanalysis approach related to defence mechanism that is related to the purpose of imaginary companion. This research is using qualitative method and descriptive analysis technique to analyse the defence mechanism that happened in the main character’s personality problems and his attachment to his imaginary companion. This analysis is done after analysing the main character using character and characterization theory by Petrie W. Dennis and Joseph M. Boggs. Based on the analysis, the writer concludes that Colin Reeds has several anxieties related to loneliness. Soon his ego solved it by creating an imaginary companion named as Faye. Colin’s creation of Faye was based on his need of a trustworthy friend caused by the frequent absence of his parents and the lack of respect by his friends. Furthermore, Faye also helped him reaching his dream of flying, playing music better, and solving the anxiety that he had towards finding new friends and the existence of Sigmund Corp in the end. This research found that Faye’s existence includes the effort to maintain Colin’s personality balanced which could possibly come from the causes mentioned above and the further that came. Through Faye’s existence, he ran several defence mechanisms to overcome his anxieties, such as fantasy, projection, introjection, suppression, regression, and repression.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".