Cancer Donation: Integrating Homo emotionalis with Homo economicus
Bibliographic record
Abstract
The same was true for other efforts as well in the food industry, up to at least 2006 (Judy Zauenbrecher, Welches's, personal communication, 2006).What seemed to emerge from these and other conversations was the fact that research done in a systematic manner to uncover rules about behavior often maintained value of years, even decades.What was of little value was the study so tightly focused that it yielded only a factoid rather than these rules.The realization led to the recognition that industrial, or better applied research, would do well to incorporate the effort to find rules.Indeed, in their 2007 book, Selling Blue Elephants authors [1] entitled the effort 'Rule Development Experimentation.' It was clear by 2007 that these studies, some twenty and thirty years old, would still yield value information to guide thinking, communication efforts, and product developments, decades later.In some respects, these rule-developing experiments were creating a sort of 'scientific literature' of a topic, albeit from the point of view of a corporation, and a specific application.The reason for this introduction is to lay the groundwork for the additional information which can emerge from these studies, information that may be presented in a cursory manner to managers tasked with the job of creating the event.Yet, Mind Genomics provides an opportunity to develop a database of deeper knowledge and insight, both to create better telethons in the future, but also to understand the topic in far greater depth, an understanding which can become systemic.It is the further exploration of data, now about 13 years old, an exploration into the principles and patterns, which Mind Genomics provides as the foundation for the future.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".