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
Within a set of relationships, large‐scale high stakes testing induces consequences for its stakeholders—intended and unintended, positive and negative—between testing, teaching, and learning. At the core of this phenomenon lies the (mis)use of test scores and of the values and stakes attached to a test in society and within the pedagogical context where a particular test exists. Washback and impact in applied linguistics refer to two levels: “impact”—the effects of tests on macro‐levels of education and society, and “washback”—the effects of tests on micro‐levels of classroom teaching and learning. Over the past two decades empirical research has established the relationship between testing and teaching; this period has seen an increasing number of studies on learning and learners, as well as on other stakeholders such as publishers, parents, and employers; and studies have increasingly focused on the importance of contextual factors in testing. The challenges for future research relate to the call for collecting validity evidence from multiple stakeholders by using multiple methods to understand this complex phenomenon.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.089 | 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