Bibliographic record
Abstract
Distributed by New Day Films, 350 North Water Street Unit 1-12, Newburgh, NY 12550; 888-367-9154Produced by Alison MountzDirected by Lisa Molomot2020, Streaming, 80 mins Safe Haven personalizes experiences of military draft resisters and anti-war objectors who flee the United States for Canada. It illustrates cases of Vietnam War and Iraq War era quests by one subset of Americans we come to know quite well throughout the film, as they recount seeking refuge in the nation to the north. A strong collection of first-person testimonials is the heart and soul here, as each resister speaks at a human level about their struggles and biographies. Particularly effective are the comparisons, some unspoken, between anti-Vietnam War and anti-Iraq War postures, when government policies shift in recent times toward a more constraining reality for that type of cross-border migration. While valuable data is shared on the wars and depictions of resistance efforts are featured, such as the Canadian War Resisters Support Campaign and Iraq Veterans Against the War, the film is not an intellectual probe into anti-war philosophies or related organizational tactics or campaigns. What is well portrayed instead, are life experiences of these specific persons who have fled the U.S. for Canada. Director Molomot has previously demonstrated a talent for introducing human struggles at crossing borders in the face of steep odds in Missing in Brooks County, her film covering migrant deaths at the U.S.–Mexican border. Outcomes in Safe Haven may be less immediately deadly but no less life changing. Themes cutting across the time periods described in Safe Haven include the variety of official lies told to citizens and societies as catalysts behind war efforts, and examples spanning generations of the resilience for those persons caught between two countries. The anti-Vietnam War resisters we see who end up serving in positions of elected or judicial office within Canada is impressive. Prison times does befall some others that we encounter in the film. On a cinematographic level, Safe Haven may slightly overuse its confessional shooting style as we meet the resisters. That is only a rare hurdle, and perhaps a few further visualizations might have lessened what may strike some viewers as overuse of talking heads who directly address the camera. Perhaps more images associated with behaviors and surroundings of resisters deserved a tiny bit more screen time, because so much of this tale the resisters share is about behavior and not analysis. The strength of what we do learn from the resisters in these reflections results in a film about war resistance where the statements embody more or less anything except strident anti-war screeds. This itself may be a chief strength in assisting the film’s quite effective demystification of the many contradictions looming when governments turn citizens into soldiers. Safe Haven is recommended as a valuable resource for those exploring U.S. - Canadian relations and also for students and scholars of human migration. It is a solid addition to the study of U.S. war resisters generally and of the Vietnam and Iraq War experiences of the United States more specifically. The film may be of added special interest to individuals or agencies who serve persons recovering from any number of traumatic war related experiences, military veterans and resisters alike. Awards:Best Documentary, Central Alberta Film Festival (2020)
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.909 | 0.739 |
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".