Bibliographic record
Abstract
Distributed by Good DocsProduced by Ina Fichman, Amy Miller, and Ariel NasrDirected by Helene Klodawsky2023, Streaming, 85 mins Stolen Time, a feature length documentary, follows powerhouse Canadian lawyer and eldercare advocate, Melissa Miller, as she investigates and builds a case of mounting evidence against long term care facility businesses. Stolen Time is a well-produced film that highlights rampant elder abuse and neglect in the long term care industry. Director Helene Klodawsky balances gut wrenching interviews from families with interviews from scholars and nursing home staff. Miller and her team conduct a thorough investigation into the lack of financial transparency and neglectful practices of some of the largest companies that oversee most long-term care facilities in Canada. Using this evidence, along with family testimonies, Miller builds a Mass Tort case that seeks to dismantle the systemic issue of elder negligence in these facilities. Stolen Time has an engaging narrative, high quality audio and visuals that will hold audiences’ attention. In an educational setting, this film would be ideal for those interested in elder care and rights, long term care facilities, and Canadian law. Awards:Award of Excellence Special Mention: Documentary Feature, Accolade Global Film Competition, La Jolla 2024; Award of Excellence: Documentary Feature Impact DOCS Award, La Jolla 2024; Award of Excellence: Use of Film / Video for Social Change Accolade Global Film Competition, La Jolla 2024; Award of Excellence: Viewer Impact: Content / Message Delivery Impact DOCS Award, La Jolla 2024
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.484 | 0.168 |
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".