Bibliographic record
Abstract
Questionnaires have long been used as a research tool in studies of Canadian English (e.g. Chambers 1994, Dollinger 2015), whether in person, in written form or more recently, digitally. In this talk, I introduce a unique type of questionnaire, the "bulletin board poster" survey. In this talk I will describe an ongoing project that I have been undertaking as a research assistant at the Strathy Language Unit at Queen's University since September 2017. During this time, I have been creating biweekly questionnaire posters on a range of topics in Canadian English – lexical, phonological and syntactic, which I display in public areas on campus. I then tabulate the data and create posters summarizing the results, which I also display on campus. The "bulletin board poster" method of data collection clearly has its limitations, such as the lack of control over who participates and how accurately they do so, but it has benefits as well, such as engaging the participants on the topic and encouraging participation in more rigorously controlled studies. In my presentation, I will discuss the stages of this project, the pros and cons of the "bulletin board poster" method and other survey methods and share some of our results. I hope to encourage discussion on this type of data collection and share my tips on how I manage to make this a successful way of collecting data.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.038 |
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".