Bibliographic record
Abstract
Distributed by Bullfrog Films, PO Box 149, Oley, PA 19547; 800-543-FROG (3764)Produced by Moby, Janice Dawe, Chris Hegedus, and Kyle VogtDirected by Liz Marshall2021, Streaming, 88 mins While Meat the Future is a decent documentary, the title is a bit misleading. It makes is sounds like what will be covered is how and why ‘clean meat’, also called ‘lab grown meat’, is the meat of the future. What the film actually covers is a brief history on the first few years of the Memphis Meats company, now called Upside Foods, as well as a partial biography on its co-founder Uma Valeti. Throughout, we hear from people who are passionate about ‘clean meat’, and counter views of those who oppose with the movement, and why. This balance was really appreciated, as there are quite a few ethical questions surrounding the issue. What is missing is the scientific and technical angle; this only briefly comes up and is quickly explained that its proprietary information. As producing ‘clean meat’ is a very new processes and product, this is somewhat understandable, however it is still a little disappointing. It does include some information on establishing U.S. federal regulations on this emerging industry. Jane Goodall does narration only at the beginning and end of the film, for just a few minutes; throughout the rest there is no narration, we just hear from the people themselves. Lastly, the copy viewed for this review didn’t include subtitles, so there’s no comment to add on their quality, but the DVD version includes English SDH captions for the deaf and hard-of-hearing, and streaming on Docuseek is available with closed captions. Meat the Future is not recommended for courses looking for a film that covers the methods and scientific or technical aspects of producing ‘clean meat’, or that describes the end product’s properties (other than taste). However, if instructors are looking for a conversation starter on bioethics, or simply bioengineering projects/industries of the future in general, this would be a good choice. Interested subject areas might include, bioengineering, STEM in general, business, entrepreneurship and possibly politics. Regardless of subject area, there is an abundance of topics for discussion and/or written responses, in terms of assignments. It is rather long, at almost an hour and a half, so it would most likely need to be watched outside of class time. Awards:Nominee, Best International Documentary & Best International Director, Melbourne International Documentary Film Festival; Crystal Pelican Award, Best Director, International Science Film Festival World of Knowledge; Best Picture Editing - Documentary, Directors Guild of Canada Awards; Nominee, Best Film - Global Health Competition, Cleveland International Film Festival; Nominee, Jury Prize for Best Documentary, Riviera International Film Festival; Nominee, Donald Brittain Award for Best Social Political Documentary, Canadian Screen Awards; Nominee, Best Sound - Non-Fiction, Canadian Screen Awards; Inspiration Award for Commitment to Filmmaking for Change, Filmocracy Fest
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.377 | 0.112 |
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".