Bibliographic record
Abstract
Distributed by The Video Project, 145 - 9th St., Suite 230, San Francisco, CA 94103; 800-475-2638Produced by Ann Shin and Hannah DoneganDirected by Chloe Sosa-Sims2022, Streaming, 82 mins Hunting in Packs gives brief insight into the lives of three female politicians dealing with the realities of life in 2020: The tension of United States politics; Brexit’s effect on the country and the safety of women; the alienation of Canadian citizens. Offering a look at some of the things that women in politics must endure is valuable, especially in a time when women's rights are being eroded. Director Chloe Sosa-Sims is admirable for showcasing women that do not all hold the same belief system but does downplay some of the harmful rhetoric that is upheld by at least one of the featured politicians. However, this documentary does focus less on their politics and more on the pressures they face as women in a male-dominated field. As featured Labour Party politician Jess Phillips says, there are a fair number of useless men in politics, but there are very few useless women – because women are not generally given a second chance, nor are they afforded the opportunity to fail upwards. This documentary is not groundbreaking by any means; it is clear that there is a gender divide in politics, as well as in the larger world. But this does offer the chance for three politicians to share some personal stories and to offer words of wisdom, as well as the stark realities of the world. There are no triumphant moments in the documentary where these women triumph over evil. Instead, these women must contend with disappointment, and making the best of the cards they have been dealt. The reality of the documentary is what makes it successful. Nothing is couched in platitudes. These women understand how bad things are, and they have to work within a broken system in order to effect change.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.703 | 0.349 |
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".