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
Abstract Activation has received an enormous amount of attention over the past decade and a half. Despite the immense academic interest, activation policies remain difficult to compare. This is notably because these policies can be adapted multiple ways and are not confined to one policy area. Furthermore, common activation indicators such as expenditures can be misleading as not all activation instruments affect spending levels. These limitations notwithstanding, states continue to create and adapt activation policies. With the objective of identifying and comparing second‐order change, the author proposes a typology of activation policies according to how they affect target population behavior through incentives. The typology first identifies the lever to the labor market, supply, or demand. Second, it determines whether the mechanism for labor market integration is financial or human capital. In so doing, it allows for a more detailed understanding of the policy instruments adopted. This can be used as a tool in qualitative analysis to identify a change in policy instruments within and between cases.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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