Does gender diversity in forest sector companies matter?
Bibliographic record
Abstract
The extant literature concerned with enhancing competitiveness of forest sector companies has focused on phenomena rather than people who would drive those phenomena. Generally, human resource management research is sparse in the forest sector literature, despite well-documented knowledge that companies succeed more because of their people than because of any other factor. This article brings human resource issues to the core conversation in forest sector competitiveness research. Specifically, we focus on the link between gender diversity in top leadership and firm financial performance and suggest pathways to improve workforce diversity. The timing of our study is fortuitous, as the graying workforce in the sector will create space for a new generation of leaders. Our argument is that diversity issues should be more proactively addressed in forest sector workforce recruitment not just because it is the right thing to do, but also because it has an underlying business case. This is an exploratory study, which is a first step in paving the way for future work on diversity management among forest sector companies. Our findings suggest that more gender diverse top management teams are associated with higher financial performance but that the level of gender diversity in boards of directors has no association with financial performance.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; both teacher heads agree on what is shown here.
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".