Identity, Investment, and Faces of English Internationally
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 This article has been developed from a keynote address given at the June 2015 Faces of English conference held at the University of Hong Kong. The article examines the trajectory of Bonny Norton’s research on identity and language learning, highlighting her construct of investment, developed as a sociological complement to the psychological construct of motivation (Norton, 2013). An important focus of the paper is the expanded 2015 model of investment (Darvin & Norton, 2015), which responds to the changing communicative landscape of an increasingly digital world, and locates investment at the intersection of identity, capital, and ideology. Norton exemplifies her theories with data drawn from her collaborative research on English language learning in Canada, Pakistan, Uganda, and Iran. With reference to digital storytelling as a promising classroom practice, she argues that the challenge for English language teachers internationally is to promote learner investment in the language and literacy practices of classrooms by increasing the range of identities available to English language learners.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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