Bibliographic record
Abstract
Abstract Evaluation is at the cusp of two urgent challenges: indigenous evaluation and sustainability. How we respond to these challenges can dramatically affect the future of evaluation. A sustainability‐ready evaluation will be transformative. It will be an evaluation that recognizes that human and natural systems are coupled, and that evaluation portfolios are now and will increasingly be affected by our connections to natural system forces including climate. Sustainability‐ready evaluation will be an evaluation that reaches well past the intervention to important public policy goals and to key sustainability challenges. Evaluating coupled human and natural systems will be challenging. Fortunately, technical barriers do not prevent us from starting to infuse sustainability into evaluation; the barriers are social and associated with the worldview and vision of evaluation. To facilitate the development of sustainability‐ready evaluation, this paper provides an initial checklist and references to useful resources. Absent transformations to become sustainability‐ready evaluation will lack relevance for many of the current and future key issues of our times. Fields lacking relevance are themselves not sustainable.
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 | 0.620 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.042 | 0.049 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.040 | 0.052 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".