Review: Reconciliation in a Michigan Watershed: Restoring Ken‐O‐Sha. By Gail GunstHeffner and David P.Warners (2024) Michigan State University Press, 283 pages
Bibliographic record
Abstract
‘Do unto those downstream as you would have those upstream do unto you Living in a world of wounds was just made a little easier after reading Heffner and Warners' uplifting yet challenging book on reconciliation ecology. Reconciliation ecology is a field of study that focuses on biodiversity conservation and restoration in human-dominated ecosystems. This story focuses on the restoration of a degraded and desecrated watershed in western Michigan, but speaks broadly about our current environmental crisis and offers promising solutions. Ecological restoration of degraded ecosystems needs more than just scientific expertise, it requires all the people in a shared place, including rural, urban, wealthy, poor, Indigenous, and immigrant, to understand each other and work together to restore wild and functioning ecosystems. The book starts with the contextual foundations of their place by describing the marvelous geological context of the watershed and its diverse temperate forests. The authors recount the lifestyles of Indigenous inhabitants who called this place home and describe the cruel history of settlement and the collision of worldviews between the Ottawa and European immigrants. The authors identified the lack of respect for both the native people and the land as the ultimate cause of environmental degradation, which includes loss of riparian vegetation and channelization, resource extraction, high sediment loads, E. coli contamination, nutrient pollution, thermal pollution, and toxic substances linked to stormwater runoff. For many centuries, the creek was known to the Anishinaabe as Ken-O-Sha, Water of the Walleye, for its clear rapids and productive fishery that sustained their communities. Now, its name reflects the extractive gypsum industry that is just one of many reasons the stream's water is polluted and dangerous to swim in. A grassroots organization called the Plaster Creek Stewards, kindled by the authors, developed a bold vision to restore the degraded watershed. The authors took their role as educators seriously by engaging directly with the local community. This was risky for two academics because these activities are not always valued by university administrators, but they recognized early on in the project that the only way to restore the water was to connect people across the watershed. It is a marvelous example of transdisciplinary scholarship between a social scientist with expertise in environmental justice (Gail Heffner) and a plant ecologist with expertise in restoration ecology (Dave Warners). The book describes six general strategies for implementing reconciliation ecology through education, research, on-the-ground restoration, establishing upstream-downstream connections, engaging faith communities, and developing future environmental leaders. Much of the groundwork involved native seed collection, raising thousands of seedlings, and outplanting seedlings in both large and small restoration projects. They demonstrate how much can be accomplished when watershed residents are invited to assist with planting trees and sedges, collecting data, and maintaining floodplains, all the while building a strong sense of community. While the title emphasizes that the work took place in a local Michigan watershed, the themes are international in scope. We all coexist with people from many educational, economic, ethnic, and spiritual backgrounds and these differences tend to divide people. This book shows how the Plaster Creek Stewards aimed to break down these differences to teach people that they are all united by the water that flows through their neighborhoods. Through humorous examples of their own mistakes and real quotes from local citizens that awoke to their own surroundings, this brutally honest and impassioned portrayal of the hard and good work that is needed to repair the land and our relationship to it should be required reading for any restoration ecologist who seeks to live an examined life and understand their place in their community and on this planet. Wendell Berry transformed the Golden Rule to apply to watershed management in the epigraph above. Let us also heed the words of Edwin Markham, ‘We have committed the Golden Rule to memory; now let us commit it to life’.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".