Baby Boy Cousins: Looking for Roots, the Essay Documentary
Bibliographic record
Abstract
How does the essay documentary curate truth, and who benefits from the personal transparency required of the essay film – the audience or the filmmaker? This research creation examines the thematic and conceptual research and created methodologies employed by Montreal filmmaker Adrian Wills to create his first essay film “Baby Boy Cousins”. Adopted as an infant, Wills embarked on a two-year journey in search of his birth family in Newfoundland for this research creation. Through real-time documentation, he uncovered the heartbreaking truth that his birth mother had taken her own life, amongst other family secrets. “Baby Boy Cousins: Looking for Roots, the Essay Documentary” investigates the essay film’s unique ability to authentically explore subjective experiences and emotions while acknowledging the limitations of objective truth. It explores the work of acclaimed essay filmmakers Sarah Polley, Alan Berliner, Chantal Akerman, and Deann Borshay Liem in relation to the construction of “Baby Boy Cousins”. Moreover, this research creation examines the intricate ethical considerations surrounding personal transparency. It investigates the delicate balance between meeting audience expectations and safeguarding the privacy of the filmmaker. “Baby Boy Cousins: Looking for Roots, the Essay Documentary” concludes with profound insights into the transformative power of personal filmmaking while acknowledging the essay filmmaker’s need for psychological self-care when engaging in personal vulnerability.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".