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
Millions of Americans, as well as millions in Europe, have used or will use a library established by Andrew Carnegie. In his lifetime Carnegie gave the equivalent of several billion dollars in today's money to establish 1,689 public libraries in the United States, Hawaii and Puerto Rico. Moreover, 660 libraries in Britain and Ireland, 125 in Canada, 17 in New Zealand, 12 in South Africa and scattered others around the world exist because of this man. 1 And this does not include the extensive positive influence of the foundations and grants established by Carnegie. Aristotle would likely have called him ‘magnificent’. Carnegie had the virtue beyond mere generosity available only to those with the means and position to benefit the polis on a grand scale. Unlike generosity, magnificence involves what Irwin has called ‘the judgment and tact that are needed for large benefactions. 2 Whether ‘magnificent’ or ‘generous’ is a better term for Carnegie's character is not my major concern. Carnegie's recent biographer simply uses ‘generous’. So, for the remainder of this paper, I will use ‘generous’. 3 But was Carnegie, in fact, generous? This paper will explore both the definition of the virtue and its application to Andrew Carnegie.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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