Perspectives on an ecosystem approach to ecogenic challenges in the Great Laurentian Basin and beyond
Bibliographic record
Abstract
During the past three centuries in North America, the science and practice of fisheries emerged pragmatically based on experiences of Native Peoples and on practices imported by invading Europeans. Achieving sustainability of high-value harvests became progressively more difficult with expansion of harvesting to low value fish and intensification of other cultural stresses that undercut natural production of the valued species. The history of fisheries has parallels in trapping, forestry, agriculture and other features of ‘development’ including disposal of wastes of all kinds. Further, adverse consequences of ‘conventional progress’ in one sector spilled over to undercut benefits in others. Features of an heuristic mindscape termed ‘ecogeny’, within which to address sustainability in evolving natural/cultural complexes, may be as old as human history. An ecogenic mindscape subsumes implicitly-shared conceptual and practical traditions in ecology, economics, ekistics, ecosophy and other eco-studies. Here ‘eco’ refers to a natural/cultural complex as a holonistic reality, both part and whole. Experimentation, reductionist analyses and quantitative models play important, but not sufficient, roles in this evolving understanding. Here I have situated my personal account, some eight decades long, within this emerging ecogenic mindscape as focused initially on fish and fisheries in the Laurentian Great Lakes Basin. Our lives are complex happenings, with events that cannot be forced into a linear spatio-temporal narrative. With many others, I participated in collaborative networks that addressed major issues in trans-cultural/trans-jurisdictional settings, each at several nested levels of ecogenic organization from local to global: identifying and correcting bad fishing and environmental practices to achieve sustainable high-value fisheries; diagnosing causes of observed degradation of aquatic ecosystems in order to suppress harmful, interacting stresses and achieve rehabilitation; strengthening earlier collaborative governance traditions among fishers and regulators; and assessing likely effects of climate warming on cold-blooded fish in their warming and otherwise stressed habitats. Large culturally-modified natural processes (sun, wind, rain) generate vast amounts of valued goods; but other culturally-modified natural processes (fire, flood, drought) also generate vast harm. Any manifestation of such goods and harms transcends the spatial boundaries of any particular private property or governmental jurisdiction. With just governance of good and/or bad ecogenic realities, both altruistic and selfish commitments need to be exercised fairly. Our networks’ policy goals were ecosystem integrity, sustainable use, just governance and caring stewardship, again at spatial levels of ecogenic organization from local to global. With ethical discourse, we engaged in adaptive co-management initiatives with iterative participation by scientific researchers, experts in computing, traditional knowledge stewards, resource harvesters, environmental users, governmental administrators, informed activists and econumenistic care-givers. Done carefully and transparently, this pragmatic kind of collaborative ‘science’ provides reliable insight that is relatively impervious to pseudo-scientific attacks from deniers promoting contrary agendas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".