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Record W287986760

Shooting Women (2008)

2012· article· en· W287986760 on OpenAlexaboutno aff
Monika Raesch

Bibliographic record

VenueFilm & history · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhotographyFilm industryPower (physics)CinematographySociologyVisual artsMedia studiesGender studiesArtMovie theater
DOInot available

Abstract

fetched live from OpenAlex

Shooting Women (2008) Directed by Alexis Krasilovsky Distributed by Women Make Movies; www.wmm.com; 54 minutes Since 1967, the Academy Awards' Cinematography category has had a total of 245 nominees. None of these has been a woman. However, a female presence has increased in this role throughout the decades. Alexis Krasilovsky's film Shooting Women provides an intimate look at the change in the role of Director of Photography over the past decades, as women camera operators have emerged and altered the landscape of the film industry worldwide. Krasilovsky's film begins with 1967 archival footage of a reporter asking an interviewee what he thinks about a woman as a camera man. The man is puzzled, proceeds laugh and eventually explains that realize[s] that [he] has a hard time answering that... a woman as a camera man. This opening scene encapsulates the underlying issues faced by women in the industry over the past several decades, as they have attempted claim their place in male-dominated production crews. The depth of the problem is, perhaps, most apparent in the reporter's phrasing: 'woman as man.' A 'camera woman' is non-existent vocabulary at the time of the interview, as are gender-neutral terms such as 'camera operator.' An array of interviews follows this scene, the majority carried out with female camera operators, concerning their experiences in the industry. While each of these women's stories is intriguing in its own right, the film's international focus significantly increases their stories' power. Viewers take a journey around the globe, discovering similarities and differences in attitudes towards and working conditions experienced by female DPs in different national cultures, including: Afghanistan, Australia, Canada, China, France, Germany, Japan, Mexico, New Zealand, Senegal, and Spain. In this way, Krasilovsky creates the implicit argument that although these cultures may be different, the experiences of women in this traditionally male occupation share significant commonalities. Main themes explored in the film include gender inequities, ethnic differences, and the day-to-day working relationships between men and women. Viewers witness how critical issues such as gender discrimination are manifested across cultures, in various ways and degrees. Interviews with Indian crew members, both male and female-including married couples where both spouses work as DPs-illustrate the strongest gender division in the documentary, with men suggesting that if a woman wants work on the set it is, nonetheless, still entirely her responsibility take care of her husband and children at the end of the day. Ashok Metha, a Bollywood DP, suggests that it is difficult for women to handle so many people and so much stuff. Sexual harassment is a second problem that strains working relationships between the sexes during production, an issue illustrated by Kirstin Glover's recollections of Arnold Schwarzenegger's repeated inappropriate touching of her on the set of Pumping Iron (1975). Erika Addis, an Australian-born DP, argues that while female members of production crews have been discriminated against, such allegations would almost be impossible prove. The film mentions a variety of organizations that have emerged over the course of the previous decades assist women in the film industry, but no background information is provided on these groups. …

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0830.019

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.

Opus teacher head0.038
GPT teacher head0.194
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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