Framework and proposed indicators for the comprehensive evaluation of inclusiveness: the case of climate change adaptation
Bibliographic record
Abstract
Inclusion has been gaining increased attention in various domains, including education and the workplace, as well as development, governance, urbanization, and innovation. However, in the context of climate change adaptation (CCA), the concept of “inclusiveness” remains comparatively underexplored, with no overarching framework available. This gap is crucial, given the global scope and multifaceted nature of climate change, which demands a comprehensive and inclusive approach. In this article, we address this deficiency by developing a comprehensive conceptualization of inclusive climate change adaptation (ICCA). Grounded in ethical analysis, our framework is presented for discussion and practical testing. We identify nine specific priority areas and propose one to two qualitative indicators for each, resulting in a suite of 15 indicators for the evaluation of ICCA policies. This research not only highlights the urgency of incorporating inclusiveness into CCA, but it also provides a practical framework by which to guide policymakers, practitioners, and researchers in this critical endeavor. By acknowledging and accommodating diverse value systems and considering the entire policy process, from conception to evaluation, we aim to foster a more inclusive and sustainable approach to CCA.
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 | 0.095 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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".