Three Paradoxes of Climate Truth for the Anthropocene Social Scientist
Bibliographic record
Abstract
Climate change has been one of the most contested truths for the past two decades. Many social scientists within the academy and this volume have spent years discerning the nature of this truth and articulating its importance for business, organizations, and society. Yet these same scholars face a triple paradox in their work on this important issue. In this essay, we examine those paradoxes—(1) The Paradox of Eliminating the Main Driver, (2) The Paradox of Objectivity and Passion, and (3) The Paradox of Double Irrelevance—and how they are amplified by two institutional factors—the construction of climate truth and its translation in relational fields. We revisit not only how the three paradoxes affect the Anthropocene social scientist as an individual, but, in light of the paradoxes and the two amplifying institutional factors, how she or he might rebalance these tensions by pushing back on each while embracing paradox in personal choices.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".