“From nude calendars to tractor calendars”: the perspectives of female executives on gender aspects in the North American and Nordic forest industries
Bibliographic record
Abstract
Increasing gender diversity is no longer just the right thing to do, but also the smart thing to do. Although there is general literature about gender diversity and the perspectives of females in top management and leadership, there are, however, very few forest sector specific studies. This exploratory study utilizes interviews to better understand how female executives in North America and the Nordic countries of Finland and Sweden perceive the impact of the situation of gender diversity in the forest industry. Respondents also provide career advice for young females entering or considering entry into the industry. Female executives in both regions agree that although the forest sector is still seen as a male-oriented industry, there are signs of increasingly positive attitudes regarding industry and company culture towards the benefits of greater gender diversity; however, the described changes represent an evolution, not revolution. Interestingly, despite the status of Nordic countries as leaders in bridging the gender gap, respondents from this region believe that there is significant progress yet to be made in the forest industry, especially at the entry level. With respect to career development, North American respondents suggested that young females should consider sacrificing their social life and leisure time activities, whereas Nordic respondents instead emphasized personal supports or using exit strategy from an unsupportive company or boss.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".