FOOLS RUSH IN: Exploratory Research, Change Moments and The Work of Fools for Health
Bibliographic record
Abstract
To Boldly Go … Where No One has Gone before .. to find the answer, you must first form the question … however, the question(s) you form, affects the answer(s) you are given.[1] ALL research is exploratory, for exploration is the basis of all good research [2].Research requires a starting point to test the waters of the question or idea being explored to find out more about its scope and shape.From this starting point, with good planning, one discovers nuances for further examination, and develops hypotheses and data to be examined.However, some research is more exploratory than others.Rather than examining the known it takes a leap of faith, a step into the unknown.And while, not all exploratory studies reveal new information, you cannot know this until you are deep into the research itself.This article examines the murky world of exploratory research through an examination of some of the change moments and discoveries in the work of Fools for Health's, what is The Value of a Smile project.In Tao All Things Are Connected My lifelong studies of Taoist and Buddhist thinking, began in1969 when I attended my first class in martial arts.From that moment on I began to understand that all things are connected
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 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".