Mentorship, knowledge transmission and female professionals in Canadian water research and policy
Bibliographic record
Abstract
We face multiple water challenges: droughts, floods, crumbling infrastructure and disappearing natural hydrosystems. Technological interventions will help but these challenges also require social solutions and good governance. Identifying and implementing both technical and social solutions demands a resilient water research and policy community (WRPC). The WRPC must include diverse perspectives as the challenges increase in intensity, frequency and scope and as decision processes accelerate. Will the WRPC be able to effectively address this evolving water context? Possibly, but we argue that the WRPC's effectiveness will be partially determined by its ability to respond to impending demographic changes and the erosion of valuable knowledge resources. Generating stronger social ties between water professionals from different generations is critical to transfer these knowledge resources. Mentorship has been recognized for both its individual benefits and its organizational benefits, yet it has been under-explored within the WRPC. Using a qualitative analysis of a Canadian case and focusing on a female professional sub-population, we argue that mentorship has significant potential to develop and sustain intergenerational ties and knowledge resource transmission within a WRPC. Our findings suggest that long-term mentorship investments will directly contribute to the WRPC's resilience and its ability to effectively address water challenges.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".