Gendered interactions in corporate annual report photographs
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the gendered nature of interactions in mixed sex Canadian corporate annual report (CAR) photographs. Design/methodology/approach Quantitative content analysis of 106 CAR photographs is performed to evaluate, at the level of the photograph, how women and men interact in mixed sex photographs to reveal their relative prominence, power and status. Findings Women in CAR photographs overall are under‐represented. In mixed sex photographs, however, the relative proportions of women and men approximate those of women in the Canadian workforce, but men are more prominent in most photographs. Mixed sex photographs are relatively similar in composition (depicting largely passive, smiling subjects, few of whom are talking or in positions of authority). Where there are differences in mixed sex photographs, however, women are portrayed as less powerful than the men in the photographs. Supplemental testing suggests that the findings are persisting over time. Originality/value This paper looks at gendered interactions in CAR photos in a Canadian context. It takes the photograph, rather than individual subjects in the photo, as the level of analysis. This research clearly situates the inclusion of photographs in CARs in the voluntary disclosure literature and explores the implications for management and readers of CARs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".