Bibliographic record
Abstract
This article addresses Indigenous film-maker, Lisa Jackson’s, skillful and strategic integration of selections of Western Art Music from the Early and Late Classical period in the soundtracks of her recent films. This strategy draws attention to indigenous perspectives on economic and cultural sustainability, as well as to the threat posed to indigenous continuity by colonialist legacies, past and present. In Jackson’s films, the excerpts from Western Art Music comprising the musical score “takes over” the narrative; their sound is pleasant, but unseen, insidious and triumphant, ultimately a duplicitous and malevolent dominating force. In my view, the selections from Western Art Music function as a metaphor for the unseen, insidious and ever-present forces of colonialism that control the negative behaviors and lives of the indigenous protagonists in the film narrative. This metaphor functions on both macro (formal and performative) and micro (melodic and chordal) levels. On a meta-narratival level, Jackson’s soundtracks draw attention to contemporary audience’s de-sensitization to the use of sonic repertoires in popular cinema and to the normalization of the congruence of sound and musical material and film narrative. Jackson’s adaptation of the musical material suggests that in order to shed colonialist legacies, we must also interrogate their often physical heard, but cognitively and critically unremarked, accompanying soundtracks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".