Bibliographic record
Abstract
The challenges that are associated with the 17 United Nations sustainable development goals are wickedly complex and interconnected in nature. Because they require transformational changes at the systems level, the pace of change has, so far, been nowhere near fast enough to meet the goals by 2030. In this paper, we analyze the catalytic actions of a novel form of organizing that could potentially facilitate the timely achievement of transformational aspirations such as the SDGs: the transformation catalyst (TC). By identifying 27 TCs and analyzing their vision, mission, values, and their practices represented on their websites, we elaborate the following four key ways that TCs are distinctive from other entities, and therefore potentially more capable of facilitating transformational changes at the systems level: (1) TCs have transformation agendas that target systems-level solutions to bring about large-scale and fundamental changes in the relevant system(s), as opposed to more incremental or fragmented approaches; (2) TCs engage in catalytic actions, such as connecting, cohering, and amplifying the work of partners and collaborators; (3) TCs clearly acknowledge the current status quo, attributions, and urgency (i.e., sensemaking) of the issues on which they focus; and finally, (4) TCs embody systems orientation. In exploring how TCs work, we hope to build a solid conceptual framework for understanding the nature of transformative catalytic action on societal issues, and consolidate our understanding of what elements are needed if TCs are to work, providing a starting point for future research.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".