Is Technology for the Anthropocene an Impossibility? A Conversation about the Myko Project
Bibliographic record
Abstract
We, the two interlocutors in this discussion, Mark Antaki and Richard Janda, have for the past number of years had periodic exchanges about the theoretical underpinnings and possible critique of a project that Janda has been leading, which seeks to signal to individuals the impacts of their choices upon collective environmental, health and social goods and to orient these individuals to make better choices. Antaki has sought to probe a number of paradoxes and challenges for legal normativity involved in using new forms of technology to address the accumulating and devastating externalities produced by our use of technology. A mutual fascination with the project and its critique led us to conclude that the discussion might have some broader saliency. This dialogue allowed us to share our preoccupations concerning the pervasive quality of technology in our lives and to explore how our efforts to redress the dominion of technology over nature might cede to the temptation to call upon new forms of technology in aid. Is this temptation to be resisted?
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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.059 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".