Investment and motivation in language learning: What's the difference?
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
The year 2020 marked the 25th year since Bonny Norton published her influential TESOL Quarterly article, ‘Social identity, investment, and language learning’ (Norton Peirce, 1995) and the fifth year since we, Darvin and Norton (2015), co-authored ‘Identity and a model of investment in applied linguistics’ in the Annual Review of Applied Linguistics. From the time Norton's 1995 piece was published, investment and motivation have been conceptually imbricated and often collocated, as they hold up two different lenses to investigate the same reality: why learners choose to learn an additional language (L2). In our 2015 article, we made the case that while it is important to ask the question, ‘Are students motivated to learn a language?’ it is equally productive to ask, ‘Are students invested in the language practices of the classroom or community?’ (Darvin & Norton, 2015, p. 37). We recognize that the relationship between language teachers and learners is unequal, and that teachers hold the power to shape these practices in diverse ways. Teachers bring to the classroom not only their personal histories and knowledge, but also their own worldviews and assumptions (Darvin, 2015), which may or may not align with those of learners. Relations of power between learners can also be unequal. As Norton and Toohey (2011, p. 421) note: A language learner may be highly motivated, but may nevertheless have little investment in the language practices of a given classroom or community, which may, for example, be racist, sexist, elitist, anti-immigrant, or homophobic. Alternatively, the language learner's conception of good language teaching may not be consistent with that of the teacher, compromising the learner's investment in the language practices of the classroom. Thus, the language learner, despite being highly motivated, may not be invested in the language practices of a given classroom.
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 | 0.002 |
| 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