Critical Conversations and A Call to Action!
Bibliographic record
Abstract
Critical Conversations are held by members of the greater Engineering, Social Justice, and Peace network in the activist tradition of reflecting on our public engagement and collectively discovering ways of deepening our action. The particpants are selected based on their submissions (Expressions of Interest) in response to the Call for Participation in the Critcial Conversations disseminated through the ESJP website (esjp.org). For years, we have gathered in locations immersed in nature. In 2018 and 2019, the gathering took place in Cala Munda, organized by Caroline Baillie and Eric Feinblatt, in the beautiful Catskills mountains in upstate New York in the U.S.A. We want to feel our connection with the land while we engage in critical conversations on the intersection of the engineering field with social justice and peace. Caroline Baillie facilitates these conversations employing forest pedagogy. Through this pedagogy, we open our hearts to the forest for seeking guidance on how our profession can help restore, heal, and serve people, planet, and life instead of its current practice of destroying, pillaging, and harming nature. In the throes of the coronavirus pandemic, the urgency of action was evident in 2020 like never before. On June 26 and 27, 2020, a group of up to 40 educators, researchers, activists, and field practitioners, from 4 continents, met virtually for the 4th Annual Critical Conversations – almost thrice as large as the 2018 and 2019 groups that met in-person. The virtual format allowed for broader participation – both in numbers as well as geographical locations. Though we were physically separated in the online gathering, situated in our respective modern, often disconnected-from-nature enclaves, our hearts and minds were engaged in envisioning transition to a just and egalitarian society. In keeping with the need of the moment, our focus was on brainstorming action projects that we can implement in the near future. The retreat facilitated the formation of action teams, which spent the summer discussing possible action items moving forward. These teams are now looking for a more permanent structure with team leaders, team members, an infrastructure, and social media presence. This is a call to action! We carried out these deliberations in an open-space format, wherein the agenda for the two days was set by the participants. In the two sessions on day one, using this participatory approach, we were able to sift six main themes that participants were interested in exploring in-depth. On day two, we divided ourselves into six teams and each team took a deeper dive into their theme of choice. Five of these teams have written summaries of their deliberations and proposed their Calls to Action for the engineering community, which we report below.
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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 | 0.062 | 0.149 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.047 | 0.063 |
| Scholarly communication | 0.038 | 0.047 |
| Open science | 0.006 | 0.047 |
| Research integrity | 0.034 | 0.056 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".