Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Part I of this series, I introduced the concept of memes (1). Memes are ideas or concepts--the information world equivalent of genes. The goal of this series of articles is to infect you with memes, so that you will assimilate, translate, and express them. No matter what our area of expertise or "-ology," we all are in the information business. Our goal is to be in the wisdom business. In the previous papers in this series, I showed that when we convert raw data into wisdom we are moving along a value chain. Each step in the chain adds a different amount of value to the final product: timely, relevant, accurate, and precise knowledge that can be applied to create the ultimate product in the value chain: wisdom. In Part II of this series, I introduced a set of memes for measuring the cost of adding value (2). In Part III of this series, I presented a new set of memes for measuring the added value of knowledge, i.e., intellectual capital (3). In Part IV of this series, I discussed practical knowledge management tools for measuring the value of people, structural, and customer capital (4). In Part V of this series, I will apply intellectual capital and knowledge management concepts at the individual level, to help answer a fundamental question: What is my added value?
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it