Bibliographic record
Abstract
Music videos are designed, filmed and edited to magnify the musical experience, and, when well-used, contribute to making artists stand out. With the evolution of media and technology, ‘localness’ can be broadcast worldwide, and folklore, culture and traditions are at the heart of many metal groups’ preoccupations. By making their culture a central part of their music, Alien Weaponry’s success has resulted in the Maori culture, history and legends achieving international recognition in the metal music world. ‘Kai Tangata’ and ‘Hatupatu’, the music videos directed by Alex Hargreaves, operate to further represent elements of Maori culture, by adding a visual dimension to Alien Weaponry’s use of te reo Maori, the Maori language. Using formal and comparative aesthetical analyses, reinforced by a theoretical approach, the use of immersion in this representation will be discussed. Firstly, the representation of the characters in the videos and their role in the narration will be analysed. Secondly, the affect and the dynamism brought by the rhythm and the structure of music and images will be examined. And finally, the representation of bodies, gestures and rituality will be analysed, as a representation of the Maori culture, meant for both Maori and non-Maori people.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".