Generating better understandings of linguistic dissociation using multiscalar temporal accounts
Bibliographic record
Abstract
Linguistic dissociation is “a relatively enduring psychosocial process in which an individual or group distances themselves from a set of linguistic practices already within their repertoire because those practices have come to connote a state of significant intersubjective disharmony, or contrasubjectivity ” (Moore, 2023, p. 1152). Generating data to theorise the nature and causes of linguistic dissociation among particular kinds of language users represents a methodological challenge; as intersubjective phenomena, linguistic dissociation and contrasubjectivity emerge across multiple converging, connected temporal scales. With reference to data generated with two participants from a larger critical realist grounded theory method investigation into the nature and causes of L1 dissociation among some Japanese-English late plurilinguals, I show how I combined two data generation activities with distinct temporal scales—a multimodal timeline through which participants constructed an account of their affective relationship to Japanese over their lifespan and a two-day language use journal—with follow-up interviews to produce different types of data about linguistic dissociation, which in turn made it possible for the participants and me to construct different forms of knowledge about the phenomenon. Further, I contend that we used those knowledges to arrive at better (i.e., more verisimilitudinous) understandings of the nature and causes of the L1 dissociation they had experienced. I finish by sharing methodological implications for applied linguists investing various intersubjective phenomena that shape the linguistic repertoire and reflecting on the limits of my own knowledge of linguistic dissociation.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".