The Common Good Happiness Project: A Spiritual Quest for the Good Life
Bibliographic record
Abstract
There is a lot going on in the UK and the rest of the world and in particular in Canada and France, amongst others, which I strongly believe is very relevant to the value-based economics/business education and globalisation for the common good that we are all committed to. The Prime Minister of Britain, David Cameron, has launched a survey to measure British people’s well-being. He has stated that "We should be thinking not just what is good for putting money in people's pockets but what is good for putting joy in people's hearts. When politicians are looking at issues they should be saying to themselves 'how are we going to try and make sure that we don't just make people better off but we make people happier, we make communities more stable, we make society more cohesive'. " The Nobel Prize Laureate in Economics, Joseph Stiglitz has recommended nations measure emotional prosperity, rather than economic prosperity as a true indicator of people’s happiness. Whilst Warren Buffett has stated that unconditional love is more valuable than any amount of wealth; and that success is getting what you want, but happiness is wanting what you get. From the dawn of our creation, our ultimate desire has been to find happiness. This desire is in
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".