Prolepsis and Rendering Futures in Intergovernmental Panel on Climate Change Reports
Bibliographic record
Abstract
Rhetorical figures of speech provide important analytical frames to chart how arguments operate within genres and within genre ecologies. Varieties of the figure prolepsis allow for the rendering of future time or fact in the present, which can be a powerful rhetorical inducement toward social and political action. In this article, we examine how anticipatory arguments drawn from complex data shape a key genre for public and policy-facing work on the climate crisis—the Intergovernmental Panel on Climate Change’s Synthesis Report’s (SYR) Statement for Policy Makers (SPM). We examine how the rhetorical figure of prolepsis operates within this genre to understand the anticipatory arguments and logics emerging from the synthesis of scientific findings and their reporting. Pairing figural studies and Rhetorical Genre Studies, we further offer an approach to investigate how these patterned operations of language might intersect in their rhetorical workings.
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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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".