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
A long‐established belief in the field of English language teaching, and society generally, characterizes native English‐speaking teachers (NESTs) as superior to non‐native English‐speaking teachers (NNESTs). This pervasive, yet unfounded, belief results in discriminatory practices against NNESTs. In an attempt to level the playing field, a substantial body of literature has examined the strengths of NNESTs and argued that teachers should be evaluated on the basis of their pedagogical qualifications, not their native/non‐native status. In a globalized world, it is important to recognize the unique linguistic repertoires of individuals and their distinct ways of communication. Hence, the long‐standing view of predetermined, fixed, and unitary linguistic identities and the attributes associated with such identities needs to be challenged. To empower themselves, all teachers, regardless of their linguistic identities, are encouraged to become aware of their own unique situated strengths and challenges and to be proactive in pursuing self‐directed, ongoing professional development.
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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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