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
FACTS.That might seem self-evident.However, when the decisions are for a very large and complex institution, or when the issue at hand is complicated or difficult to measure, facts can be hard to come by.In many instances, decisionmakers are so used to being unable to get much useful data that the standard operating procedure is to proceed on the basis of experience.My career has been primarily in the business world, where the advent of the computer has made data dramatically more usable and accessible than ever before.I've worked to build systems that collect and translate this data into information ready for analysis.And I've seen the dramatic effect it can have on focusing efforts, making improvements and measuring results.When I began working with the Chicago Pubic Schools, one of my contributions was to apply my experience in transforming and managing large organizations through the use of information.CPS had warehouses full of data.Unfortunately, in warehouses it was inaccessible and therefore almost useless.I think one of my most significant accomplishments at CPS was building a technology infrastructure that allows for ready access to and the use of data.Melissa Roderick, SSA's Hermon Dunlap Smith Professor, has been at the forefront of showing how the information that's now available at CPS can help us make better decisions about public education in Chicago.Her study of high school dropouts as the co-director at the Consortium on Chicago School Research, for example, found that statistically, if a student finished freshman year, they were much more likely to graduate high school.Those findings had a significant impact on CPS strategies, from the introduction of the Freshman Connection program to intense efforts to guide students through that first critical freshman year.This approach to research-to find the real story in the data, to test assumptions, to use information to create evidence-based practice-is part of the strength and innovation at SSA. Faculty at the School are combing through data to find new, better information about everything from stopping gun violence to building better substance abuse treatment programs."Bottom line" can sound like a harsh term when it comes to social services and social justice-but it can mean more than just judging fiscal costs.It can also mean a thorough, scientific look at conditions and results in the real world.In the very best sense of the term, SSA gets to the bottom line of the policies and practices in place to solve society's toughest, most important issues.I am consistently impressed with the work going on at the School to use data-facts-to help shape the effectiveness of policies that improve our society.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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