Investment and motivation in language learning: What's the difference?
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.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".