Bibliographic record
Abstract
Canadian artist Patsy Gallant (born 1948), who achieved worldwide commercial success as a disco queen, released Take Another Look in 1984. The album tells the story of the “High Tech Girl,” a female cyborg character that serves as a metaphor for Patsy herself to address the imminent end of her marriage to musician Dwayne Ford through the album. For the first time in the singer’s discography, synthesizers and MIDI sequencers took a predominant role. Those new digital technologies inspired the construction of a futuristic piece where distinct elements such as the sleeve photos, the lyrics, the musical arrangements, and the final sound interacted with each other according to an unevenly organized concept. Sound effects and repeating short musical loops were created with the synthesizers to represent narrative elements through the tracks. This article combines interpretative studies, musical analysis, semiotics, and studies related to Patsy Gallant’s biography (as well as her accounts of the making of the album during interviews and conversations with the author) to investigate the intersections between Take Another Look and the background music presented in early home console games, as well as the overall influences of early video game music on the album. It takes into consideration some concepts featured in video game design and game audio literature, notably studies of game narrative, the function of audio elements on gameplay, and game semiotics. In the article, Take Another Look is highlighted as an innovative album where video games are important narrative and sonic references. The language of video games impacts the artistic concept of the record and the metaphors related to both the construction of Patsy Gallant’s musical persona and the music for the project.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".