Bibliographic record
Abstract
The purpose of this dissertation is to study the intertextual references drawn from cultures and practices in our world that are incorporated into the formation of the fictional world of the Nickelodeon animation Avatar: The Last Airbender. This animation that was created and produced by Michael Dante DiMartino and Bryan Konietzko for Nickelodeon has enjoyed and garnered much success worldwide. This qualitative research uses the conceptual framework of intertextuality to code the data gathered from the animation into a manner that best illustrates the animation’s intertextual references that are drawn from the cultures in our world. The research design was specifically created by the researcher to analyze references of landscapes and architecture, clothing and lifestyles in the animation in terms of their visual and verbal references. The results show that these references were drawn from the Inuit, Native American, Chinese, Japanese, Tibetan and Bhutanese cultures. This study would reveal how these numerous references are realized and exhibited in the animation as well as increase the limited literature that has been conducted upon this genre. Keywords: Intertextuality, Animation, Avatar: The Last Airbender
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".