How Do We Do Good While Doing Well? Studying the Consequences of Markets in Tackling Social Problems
Bibliographic record
Abstract
As practitioners addressing issues related to international development, global poverty, and the UN Sustainable Development Goals move toward using market-based approaches to distribute financing, products, and services, management scholars seek to understand the potential consequences. In this symposium, four scholars explore the following questions: What are the manifest and latent consequences of utilizing markets and businesses to address development goals? How can management scholarship be used to think through ways to mitigate the negatively outcomes that disproportionately affect vulnerable populations while amplifying the positive ones? The Business of Health: Identifying and Overcoming Barriers to the Success of Health Enterprises Presenter: Emily Barman; Boston U. The Role of Interpersonal Interactions in Shaping Social and Economic Development Presenter: Laura Doering; U. of Toronto Meeting the Needs of the Poor Presenter: Aneel Karnani; U. of Michigan, Ann Arbor Understanding Impact Investing: A New Categorical Imperative Presenter: Tyler Wry; The Wharton School, U. of Pennsylvania
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".