Bibliographic record
Abstract
<JATS1:p>Animator Norman McLaren is best known for his experimental films using pioneering techniques and his work as founder of the animation department of the National Film Board of Canada (NFB), but little mention is made of his Scottish heritage or his personal life. Nichola Dobson examines some of the key events and people in his life through a close examination of his key works and his personal papers, and discusses how influential they were. By using archive material to discover his personal identity and close readings of his films, Norman McLaren rediscovers one of the most important figures in animation history.</JATS1:p> <JATS1:p>Divided into thematic chapters of significant areas of influence, Dobson analyzes his formative years growing up in Scotland and his relationship with fellow Scot, John Grierson; the international travel which influenced him politically and creatively; the creative arts which played a vital part of his life; his collaborations with other artists and his complex, and rarely discussed, personal life. Each of these chapters considers his key films during those periods with a close detailed analysis and a further examination of his life through his correspondence with family and close friends. By featuring this previously un-published material, the book allows much of the consideration of the work to be in McLaren’s own words and offers a deep insight into his vast output of films over nearly 50 years.</JATS1:p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.129 | 0.052 |
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".