Bibliographic record
Abstract
Distributed by Take Action FilmsProduced by Nadia SandhuDirected by Andrew Nisker2024, Streaming, 87 mins In 2007 Nisker released the film, Garbage! The Revolution Starts at Home which examined how household trash and climate change are interrelated. Eighteen years later This Film is Garbage! revisits themes addressed in this earlier film to see if anything has improved. The filmmaker notes almost immediately that many of the environmental issues identified in his previous film continue to pose a threat. Set in Canada, Nisker debunks the idea that Canada is an environmentally friendly country. He notes that Canadians produce some of the highest numbers of trash globally. As in the earlier film, the filmmaker finds a family willing to keep their household trash for three months. Two families participate in this trash-retaining experiment. One family lives in the city of Toronto while the other resides in a suburban setting. They both track their trash and recycling to see how much they use or throw away during this period. This Film is Garbage! explores many areas that impact the environment adversely including food waste, water pollution, plastics, illegal dumping, e-waste, the timber industry, the toy industry, and the tech industry. It includes sobering facts and statistics - food waste is the third largest cause of world gas emissions, millions of microplastics have been found in the Great Lakes, 80% of donated clothes are not wearable and are often dumped in the ocean. The documentary discusses the poor choices humans make (frequently to secure profits) that harm the atmosphere and waterways. It also reveals that actions usually viewed as environmentally mindful such as recycling, clothing donations, and driving EV cars often contribute to a myriad of problems such as “waste colonialism” or greenwashing. Despite the discouraging figures and environmental setbacks addressed in This Film is Garbage!, the discussions the filmmaker included in this documentary help it maintain a positive outlook. Nisker interviews a variety of experts trying to combat the garbage crisis. These individuals include CEOs, entrepreneurs, activists, scientists, clothing designers, and waste management experts who strive to make the world a greener place. Interviewees discovered innovative ways to repurpose old clothes when designing new outfits, invented filters that prevent microplastics from permeating water systems, mined minerals from recycled e-waste, and opened “repair cafés” which offer fix-it services for items normally tossed into landfills. The suburban family featured in the film who tracked their trash for three months became more aware of what they consumed and began composting to further reduce their waste. Although this film often includes disheartening facts related to the human trash footprint, the filmmaker remains optimistic. He believes that the green revolution starts at home, and he aspires to a greener, more sustainable future. Nisker is candid, personable, and frequently includes funny scenes captured during filming (such as a conversation during an illegal dumping stakeout where he describes the Canadian dish poutine). This Film is Garbage! is recommended for college curricula in disciplines such as biology, conservation, or ecology, but could also be used in college/advanced high school classes which focus on current events or impactful topics including public speaking or English composition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.493 | 0.193 |
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".