Bibliographic record
Abstract
In this chapter we examine conceptually how we have formed an online narrative inquiry teacher education discourse community over the last year and a half with emphasis on: (1) the beginning process of developing and inviting an online community; (2) the trials and tribulations of building an online community, and; (3) the dialogue process and the obstacles of dialogic interaction online. We focus our attention on building a safe community and use Schwier’s (2002) framework and elements of community building for online collaboration and dialogue. In doing so, we draw on our methodological work in narrative inquiry as well as other scholars’ work in creating communities (Campbell, Schwier, & Kenny 2005; Connelly 2011; Craig 2011). We further use the ‘combined communal interplay’ of dialogue (Weigand 2010), and how to begin such a process online, to define the process, dilemmas, and complexities that we work through and conceptualize in this chapter.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".