Turning the Impossible into the Possible
Bibliographic record
Abstract
An overview of the concepts of changemaking-for good (C4G) and social entrepreneurship is provided. Changers-for-good are individuals who want to change dysfunctional social situations to improve the conditions of the addressees. C4G are not only empathetic with their target population, but also compassionate. As empathy may be also used for negative acts (e.g., manipulation), and compassion per se may have no cognition of the feelings of the other, there emerges a need for a blended phenomenon: empassion. The Empassion Scale (ES) demonstrates good reliability and validity; it also correlates significantly with empathy and compassion scales. An example is Elisabeth Fry of the British Victorian era. She visited prisons for women and introduced changes in the system, providing better care for inmates and the accompanying children. The most representative kind of changemaking-for-good is social entrepreneurship; the term was coined in 1980 by William Drayton, the founding CEO of the global organization Ashoka: Everyone a Changemaker. There seem to be several paradoxes embedded in social entrepreneurs’ approach: First, they merge in-the-air dreaming with down-to-earth ways of implementation. Second, they successfully address protracted, insurmountable problems. Third, they find innovative methods to make it happen. Examples from Kenya, USA, Canada, and Bangladesh are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".