Focus Group on Computer Tools Used for Professional Writing and Preliminary Evaluation of LinguisTech
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
This paper focuses on computer writing tools used during the production of documents in a professional setting. Computer writing tools include language technologies, for example electronic dictionaries and text correction software, as well as information and communication technologies, for example collaborative platforms and search engines. As we will see, professional writing has become an entirely computerised activity. First, we report on a focus group with professional writers, during which they discussed their experience using computer tools to write documents. We will describe their practices, point out the most important problems they encounter, and analyse their needs. Second, we describe LinguisTech, a reference web site for language professionals (translators, writers, language instructors, etc.) that was launched in Canada in September, 2011. We comment on a preliminary evaluation that we conducted to determine if this new platform meets professional writers ’ needs. 1
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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 | 0.000 |
| 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