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
It is ironic that at a time when we have more quantitative data about consumers than ever before – so-called “big data,” scanner data, loyalty program purchase histories, trails of Internet searches and social media activity, and much more – that businesses nevertheless increasingly desire qualitative information. The two sets of methods also differ in their underlying assumptions about the nature of reality, the nature of evidence, causality, and factors that shape behavior. Unfortunately these differences often evoke an “either/or” approach on the part of researchers and audiences for their research. In both academic and applied research it is usually far more beneficial to adopt a “both/and” perspective and to select the best tool for the problem at hand. Otherwise, with a tool kit comprised of only a single tool, the “law of the hammer” tends to apply. If you only have a hammer in your tool kit, everything starts to look like a nail and we keep pounding away, regardless of the nature of the problem at hand.
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 | 0.003 | 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 | 0.000 | 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 | 0.004 |
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