(Dia)lects in the 21st century: Selected papers from Methods in Dialectology XVII
Bibliographic record
Abstract
In this paper, we discuss our experiences conducting a longitudinal research studyinvolving trend and panel data in the 21st century. Our goal is to document bothchallenges and opportunities for researchers engaging in real-time community-based research. The data for this project come from the Language in Later Lifeproject, an interdisciplinary research project investigating the language of healthyadults in Toronto during the transition from later life to retirement. We focus onstrategies for recruiting panel speakers and finding matches for our trend sample.One key finding is that it is not just crucial to find ways to keep in touch withparticipants, but also with research assistants and fieldworkers, who were essential for (re-)locating participants almost twenty years after the first point of datacollection. While our project was conducted in 2018–2019, i.e., before the COVID-19 pandemic, we argue that many of the obstacles we encountered still apply, andmight even be exacerbated, making it more important than ever to reflect on ourmethods for tracking language change in real time.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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; both teacher heads agree on what is shown here.
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".