Original Article: Women's “Choices” and Canadian Water Research and Policy: A Study of Professionals' Careers, Mentorship, and Experiential Knowledge
Bibliographic record
Abstract
This article is an investigation of the different factors that potentially influence the career choices of Canadian female professionals working in water research and policy (WRP). This community was broadly defined as any Canadian engineers, technicians, biologists, planners, economists, scholars conducting physical and social research, public servants (e.g., national, provincial, municipal), and civil society activists who were self-identified as working on water-related issues. Participants' essay responses were assessed by using an integrated comparative framework—drawing insights from economics, social network theory, environmental psychology, innovation, knowledge management, and pro-environmental behavior. Focus was placed on participants' responses about what motivated their careers, how this motivation sustained their professional participation over time, and whether different experiences with people and/or nature influenced their contributions to Canadian WRP. The data analysis indicated that female professionals draw on their relationships and experiential knowledge to make career decisions, sustain their career progression, and direct their career contributions. The analysis suggested that both recruitment and retention within the water community could be improved by providing recognition of alternative knowledge opportunities, including opportunities to develop skill mastery over existing or new skills, and experiential knowledge in nature for children, and by facilitating mentorship relationships and social networks. By doing so, these interventions would help sustain the availability of diverse knowledge resources held by female professionals within Canadian WRP.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".