Climate Change and the Victim Slot
Bibliographic record
Abstract
In between forays into the oil belt and conferences with oilmen, I conducted ethnography within Port of Spain’s climate intelligentsia. I apply this term to a loosely linked group of professionally successful men and women, born in Trinidad and belonging to African and Indian ethnicities. All had earned bachelor’s degrees, and many had studied further in the United States, Canada, or Britain. They knew the facts of climate change, and they cared enough to join public discussions about it. To these scientists, activists, policy makers, and energy specialists, I introduced myself as a fellow traveler: an environmental anthropologist writing a book on energy policy. Together, in 2010, we participated in a round of public consultations on the country’s first policy regarding climate change. The participants might have considered carbon emissions and means of reducing them. Instead, the consultations and the policy centered on impacts: environmental hazards, including even threats to oil’s infrastructure. In a fashion I had not anticipated, my informants positioned the petrostate of Trinidad and Tobago as simply a victim of climate change. With these informants, my conversations sometimes bordered on arguments, as instructive as they were contentious. No one broke off contact, and all seemed to consider our debate one worth having. I kept probing for an answer to the question: How and why did the climate intelligentsia frame the country as unequivocally innocent? Innocence, after all, amounts to a license to pollute. Fortunately, though, some Trinidadian public figures are beginning to reconsider hydrocarbons in ways both painful and humane.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.051 | 0.035 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".