Ecological Economics and Systems Thinking | Katie Kish + David Mallery | (ST-ON 2021-10-18)
Bibliographic record
Abstract
In conversation with the Systems Thinking Ontario community, Katie Kish and David Mallery led a discussion on Ecological Economics in two parts: (1) Where is Ecological Economics going with Systems Thinking? In the "Critical Pluralism" paper (see below), the newest generation of EE scholars is portrayed as taking a regenerative approach to research and learning. This is best navigated with critical pluralistic approaches well-developed in systems thinking. The shift might be better supported through a wider set of systems tools, which might also have complementary effects on systems methodologies. (2) What could a 30-year research agenda for Ecological Economics be? The "Paying Attention" paper (see below), is one in a special section of "Ecological Economics: The next 30 years". The original announcement for this session is at https://wiki.st-on.org/2021-10-18 . Biographies: * Katie Kish is the Senior Development Officer for the Footprint Data Foundation through the York Footprint Initiative and lecturer of Ecological Economics at the Haida Gwaii Institute, UBC. Her career has largely focused on capacity building for the international ecological economics community and training emerging scholars on the effective use of systems methodologies. * David Mallery is currently an instructor in the Ecological Economics course at York University. He is a doctoral candidate in the Faculty of Environment and Urban Change, examining the epistemological predicaments associated with mainstream quantitative methodologies informing environmental and economic policy. Suggested pre-reading: Topic 1: * Kish, Katie, David Mallery, Gabriel Yahya Haage, R. Melgar-Melgar, M. Burke, C. Orr, N. L. Smolyar, S. Sanniti, and J. Larson. 2021. “Fostering Critical Pluralism with Systems Theory, Methods, and Heuristics.” Ecological Economics 189 (November): 107171. https://doi.org/10.1016/j.ecolecon.2021.107171 . (cached on academia.edu ) Topic 2: * Kish, Kaitlin. 2020. “Paying Attention: Big Data and Social Advertising as Barriers to Ecological Change.” Sustainability 12 (24): 10589. https://doi.org/10.3390/su122410589 . (cached on academia.edu ) * Farley, Joshua, and Kaitlin Kish. 2021. “Ecological Economics: The next 30 Years.” Ecological Economics 190 (December): 107211. https://doi.org/10.1016/j.ecolecon.2021.107211 . (special section in private cache, released on registration on Eventbrite).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.389 | 0.007 |
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; both teacher heads agree on what is shown here.
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".