Karen Bakker: Honouring a Sharp Mind, Fierce Intellect, Thought Pioneer, Heart-Led Scientist and Friend
Bibliographic record
Abstract
We were all lucky to know Karen in different ways and to work with her in various capacities at different times of her life. We all lost Karen too soon – she had so much to share and give to her friends and family, to her community, and to the sharing of ideas and insights in general. She had already accomplished so much in her 51 years, but in many ways she was still budding in her knowledge and approach and in the audiences she reached. Karen Bakker’s legacy goes far beyond her conceptual contributions to the discipline of geography and the associated fields of political ecology and water governance, although these are what she will certainly also be remembered for. As such, it is fitting that we share a tribute to her in an academic journal, notably one focused on water governance. We each responded to a few questions regarding Karen’s impact on our work and our lives. What follows are the responses we each gave. They are shaped by the various relationships we had with her over the years as student, colleague, supervisor and mentor. We each thought about what inspired us most about Karen’s approach to scholarship and to life in general and about her conceptual contributions, to which we have found ourselves returning over the course of our own lives and careers. We explain how and when her work and presence in our lives has been significant, what it catalysed for us personally, professionally, and in broader societal activities in which we were jointly involved with Karen. We conclude with some joint reflections on Karen’s broader impact within academia and in the world and on her ongoing legacy in both realms.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".