Authorship in IPCC AR5 and its implications for content: climate change and Indigenous populations in WGII
Bibliographic record
Abstract
This essay examines the extent to which we can expect Indigenous Knowledge, understanding, and voices on climate change ('Indigenous content') to be captured in WGII of the IPCC Fifth Assessment Report (AR5), based on an analysis of chapter authorship. Reviewing the publishing history of 309 chapter authors (CAs) to WGII, we document 9 (2.9%) to have published on climate change and Indigenous populations and involved as authors in 6/30 chapters. Drawing upon recent scholarship highlighting how authorship affect structure and content of assessment reports, we argue that, unaddressed, this will affect the extent to which Indigenous content is examined and assessed. While it is too late to alter the structure of AR5, there are opportunities to prioritize the recruitment of contributing authors and reviewers with expertise on Indigenous issues, raise awareness among CAs on the characteristics of impacts, adaptation, and vulnerability faced by Indigenous peoples, and highlight how Indigenous perspectives can help broaden our understanding of climate change and policy interventions.
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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 | 0.047 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".