Bibliographic record
Abstract
How to use finance as a tool to build a more equitable and sustainable society. Money defines our present and will shape our future. Every investment decision we make adds a chapter to the story of what our world will look like. Although the idea of mission-based finance has been around for decades, there is a gap between organizations' stated intention to “do good” and meaningful impact. Still, some are succeeding. In Just Money, Katrin Kaufer and Lillian Steponaitis take readers on a global tour of financial institutions that use finance as a force for good. Kaufer and Steponaitis visit a bank in Europe that bases its business model on full transparency; a credit union in Canada that designed an alternative to payday lending for its community; and microfinance institutions in El Salvador and Bangladesh that provide financing to small-business clients who do not have access to the mainstream banking system. They discuss what it takes to build and operate a mission-focused business, whether the Just Banking model is scalable in the face of systemic barriers, and how to assess impact effectively. Finally, they introduce the logic of ecosystem finance, in which business decisions align with societal needs. Doing so requires more than adding impact indicators; it requires developing a new business model. With Just Money, Kaufer and Steponaitis remind us that money, if used intentionally and equitably, can be just money—a tool that serves nature, human development, and social justice.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.058 |
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".