INTERVIEW WITH DEVON WOODS / Entrevista com Devon Woods
Bibliographic record
Abstract
On May 27th, 2021, we met virtually with Devon Woods. We asked him about his studies on beliefs and the relationship between beliefs and emotions and identity. We approached the decision-making process and its relationship to BAK (Beliefs, Assumptions and Knowledge). We also, discussed its relevance to English teaching communities, particularly here in Brazil. Devon Woods has always demonstrated to be amazed by the unseen and often unnoticed processes of interpretation that are involved in both personal and pedagogical types of communication. In the 1970s, he engaged in studies of learners’ and teachers’ cognition – their beliefs, interpretations and actions – and the interactions between them in language classrooms. As a product of his 1992 doctoral dissertation, we find his remarkable book “Teacher Cognition in Language Teaching” (1996), several articles, and the focus of the work of a number of graduate students published in the Carleton Papers in Applied Language Studies. The results of this conversation are what we present here in the form of an interview.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".