Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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 it